Generated: 2026-01-12 12:36:55 UTC
def _tokenize(
self, texts: list[str], chunk_size: int
) -> tuple[Iterable[int], list[list[int] | str], list[int], list[int]]:
"""Tokenize and batch input texts.
Splits texts based on `embedding_ctx_length` and groups them into batches
of size `chunk_size`.
Args:
texts: The list of texts to tokenize.
chunk_size: The maximum number of texts to include in a single batch. def _generate(
self,
messages: list[BaseMessage],
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
self._ensure_sync_client_available()
payload = self._get_request_payload(messages, stop=stop, **kwargs)
generation_info = None
raw_response = None
def _construct_responses_api_payload(
messages: Sequence[BaseMessage], payload: dict
) -> dict:
# Rename legacy parameters return encoding_model.encode(text)
def get_num_tokens_from_messages(
self,
messages: Sequence[BaseMessage],
tools: Sequence[dict[str, Any] | type | Callable | BaseTool] | None = None, )
return RunnableMap(raw=llm) | parser_with_fallback
return llm | output_parser def _create_chat_stream(
self,
messages: list[BaseMessage],
stop: list[str] | None = None,
**kwargs: Any,
) -> Iterator[Mapping[str, Any] | str]:
chat_params = self._chat_params(messages, stop, **kwargs)
if chat_params["stream"]:
if self._client:
yield from self._client.chat(**chat_params) )
return RunnableMap(raw=llm) | parser_with_fallback
return llm | output_parser
def _generate(
self,
messages: list[BaseMessage],
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
stream: bool | None = None, # noqa: FBT001
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if _is_huggingface_textgen_inference(self.llm): )
return RunnableMap(raw=llm) | parser_with_fallback
return llm | output_parser
response = self.client.messages.create(
messages=self._format_messages(prompt), return messages
def _call(
self,
prompt: str,
stop: list[str] | None = None, def _generate(
self,
messages: list[BaseMessage],
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
) ) from e
def bind_tools(
self,
tools: Sequence[dict[str, Any] | type | Callable | BaseTool],
*, )
return RunnableMap(raw=llm) | parser_with_fallback
return llm | output_parser
env.pop("VIRTUAL_ENV", None)
subprocess.run(
["uv", "sync", "--dev", "--no-progress"], # noqa: S607
cwd=destination_dir, return replacements
@integration_cli.command()
def new(
name: Annotated[
str,
typer.Option(
help="The name of the integration to create (e.g. `my-integration`)",
prompt="The name of the integration to create (e.g. `my-integration`)",
@integration_cli.command()
def new(
name: Annotated[
str,
typer.Option(
uvicorn.run(
app_str,
host=host_str, typer.echo(f"Running: pip install -e \\\n {cmd_str}")
subprocess.run(cmd, cwd=cwd, check=True) # noqa: S603
chain_names = [] )
@app_cli.command()
def add(
dependencies: Annotated[
list[str] | None,
typer.Argument(help="The dependency to add"),
] = None,
*,
@app_cli.command()
def add(
dependencies: Annotated[
list[str] | None,
typer.Argument(help="The dependency to add"),
uvicorn.run(
script,
factory=True, if with_poetry:
subprocess.run(["poetry", "install"], cwd=destination_dir, check=True) # noqa: S607
package_cli = typer.Typer(no_args_is_help=True, add_completion=False)
@package_cli.command()
def new(
name: Annotated[str, typer.Argument(help="The name of the folder to create")],
with_poetry: Annotated[ # noqa: FBT002
bool,
typer.Option("--with-poetry/--no-poetry", help="Don't run poetry install"),
] = False,
@package_cli.command()
def new(
name: Annotated[str, typer.Argument(help="The name of the folder to create")],
with_poetry: Annotated[ # noqa: FBT002
bool, ) -> Any:
return self._model(config).invoke(input, config=config, **kwargs)
"""
tool_name = request.tool_call["name"]
# Check if this tool should be emulated
should_emulate = self.emulate_all or tool_name in self.tools_to_emulate
if not should_emulate:
# Let it execute normally by calling the handler
return handler(request)
# Extract tool information for emulation
tool_args = request.tool_call["args"]
tool_description = request.tool.description if request.tool else "No description available"
# Build prompt for emulator LLM
prompt = (
f"You are emulating a tool call for testing purposes.\n\n"
f"Tool: {tool_name}\n"
f"Description: {tool_description}\n"
f"Arguments: {tool_args}\n\n"
f"Generate a realistic response that this tool would return "
f"given these arguments.\n"
f"Return ONLY the tool's output, no explanation or preamble. "
f"Introduce variation into your responses."
)
# Get emulated response from LLM
response = self.model.invoke([HumanMessage(prompt)])
# Get emulated response from LLM
response = self.model.invoke([HumanMessage(prompt)])
# Short-circuit: return emulated result without executing real tool try:
result = subprocess.run( # noqa: S603
cmd,
capture_output=True, raise ValueError(msg) from None
return full_path
def _ripgrep_search(
self, pattern: str, base_path: str, include: str | None
) -> dict[str, list[tuple[int, str]]]:
"""Search using ripgrep subprocess."""
try:
base_full = self._validate_and_resolve_path(base_path) return full_path
def _ripgrep_search(
self, pattern: str, base_path: str, include: str | None
) -> dict[str, list[tuple[int, str]]]:
"""Search using ripgrep subprocess.""" def _create_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
"""Generate summary for the given messages."""
if not messages_to_summarize:
return "No previous conversation history."
trimmed_messages = self._trim_messages_for_summary(messages_to_summarize)
if not trimmed_messages:
return "Previous conversation was too long to summarize."
# Format messages to avoid token inflation from metadata when str() is called on
# message objects def count_tokens(messages: Sequence[BaseMessage]) -> int:
return request.model.get_num_tokens_from_messages(
system_msg + list(messages), request.tools
) def count_tokens(messages: Sequence[BaseMessage]) -> int:
return request.model.get_num_tokens_from_messages(
system_msg + list(messages), request.tools
)
edited_messages = deepcopy(list(request.messages))
for edit in self.edits:
edit.apply(edited_messages, count_tokens=count_tokens)
return handler(request.override(messages=edited_messages))
def count_tokens(messages: Sequence[BaseMessage]) -> int:
return request.model.get_num_tokens_from_messages(
system_msg + list(messages), request.tools
)
edited_messages = deepcopy(list(request.messages))
for edit in self.edits:
edit.apply(edited_messages, count_tokens=count_tokens)
return await handler(request.override(messages=edited_messages))
print_text(name, end="\n")
output = chain.run(text)
print_text(output, color=self.chain_colors[str(i)], end="\n\n") print_text(name, end="\n")
output = chain.run(text)
print_text(output, color=self.chain_colors[str(i)], end="\n\n") def compare(self, text: str) -> None:
"""Compare model outputs on an input text.
If a prompt was provided with starting the laboratory, then this text will be
fed into the prompt. If no prompt was provided, then the input text is the
entire prompt.
Args:
text: input text to run all models on.
"""
print(f"\033[1mInput:\033[0m\n{text}\n") # noqa: T201 ) -> list[Document]:
docs = self.base_retriever.invoke(
query, def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
**kwargs: Any,
) -> list[Document]:
docs = self.base_retriever.invoke(
query,
config={"callbacks": run_manager.get_child()},
**kwargs, retriever_docs = [
retriever.invoke(
query, """
re_phrased_question = self.llm_chain.invoke(
query, def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> list[Document]:
"""Get relevant documents given a user question.
Args:
query: user question
run_manager: callback handler to use retriever_docs = [
retriever.invoke(
query, """
queries = self.generate_queries(query, run_manager)
if self.include_original: def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> list[Document]:
"""Get relevant documents given a user query.
Args:
query: user query
run_manager: the callback handler to use. def save_context(self, inputs: dict[str, Any], outputs: dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
input_str, output_str = self._get_input_output(inputs, outputs)
self.chat_memory.add_messages(
[
HumanMessage(content=input_str),
AIMessage(content=output_str),
],
)
async def asave_context( def load_memory_variables(
self,
inputs: dict[str, Any],
) -> dict[str, list[Document] | str]:
"""Return history buffer."""
input_key = self._get_prompt_input_key(inputs)
query = inputs[input_key]
docs = self.retriever.invoke(query)
return self._documents_to_memory_variables(docs)
async def aload_memory_variables( def predict_new_summary(
self,
messages: list[BaseMessage],
existing_summary: str,
) -> str:
"""Predict a new summary based on the messages and existing summary.
Args:
messages: List of messages to summarize.
existing_summary: Existing summary to build upon.
def from_messages(
cls,
llm: BaseLanguageModel,
chat_memory: BaseChatMessageHistory,
*,
summarize_step: int = 2,
**kwargs: Any,
) -> ConversationSummaryMemory:
"""Create a ConversationSummaryMemory from a list of messages.
Args: def save_context(self, inputs: dict[str, Any], outputs: dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
super().save_context(inputs, outputs)
self.buffer = self.predict_new_summary(
self.chat_memory.messages[-2:],
self.buffer,
)
def clear(self) -> None:
"""Clear memory contents."""
super().clear() def load_memory_variables(self, inputs: dict[str, Any]) -> dict[str, Any]:
"""Load memory variables.
Returns chat history and all generated entities with summaries if available,
and updates or clears the recent entity cache.
New entity name can be found when calling this method, before the entity
summaries are generated, so the entity cache values may be empty if no entity
descriptions are generated yet.
"""
# Create an LLMChain for predicting entity names from the recent chat history: def save_context(self, inputs: dict[str, Any], outputs: dict[str, str]) -> None:
"""Save context from this conversation history to the entity store.
Generates a summary for each entity in the entity cache by prompting
the model, and saves these summaries to the entity store.
"""
super().save_context(inputs, outputs)
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else: return ["entities", self.chat_history_key]
def load_memory_variables(self, inputs: dict[str, Any]) -> dict[str, Any]:
"""Load memory variables.
Returns chat history and all generated entities with summaries if available, }
def save_context(self, inputs: dict[str, Any], outputs: dict[str, str]) -> None:
"""Save context from this conversation history to the entity store.
Generates a summary for each entity in the entity cache by prompting ) -> Any:
return self._model(config).invoke(input, config=config, **kwargs)
tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
observation = ExceptionTool().run(
output.tool_input,
verbose=self.verbose, if run_manager:
run_manager.on_agent_action(output, color="green")
tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
observation = ExceptionTool().run( def plan(
self,
intermediate_steps: list[tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> AgentAction | AgentFinish:
"""Based on past history and current inputs, decide what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with the observations. def plan(
self,
intermediate_steps: list[tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> list[AgentAction] | AgentFinish:
"""Based on past history and current inputs, decide what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with the observations. def _iter_next_step(
self,
name_to_tool_map: dict[str, BaseTool],
color_mapping: dict[str, str],
inputs: dict[str, str],
intermediate_steps: list[tuple[AgentAction, str]],
run_manager: CallbackManagerForChainRun | None = None,
) -> Iterator[AgentFinish | AgentAction | AgentStep]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices. return self.input_keys_arg
def plan(
self,
intermediate_steps: list[tuple[AgentAction, str]],
callbacks: Callbacks = None, return self.input_keys_arg
def plan(
self,
intermediate_steps: list[tuple[AgentAction, str]],
callbacks: Callbacks = None, if self.legacy and hasattr(self.retry_chain, "run"):
completion = self.retry_chain.run(
prompt=prompt_value.to_string(), else:
completion = self.retry_chain.invoke(
{ if self.legacy and hasattr(self.retry_chain, "run"):
completion = self.retry_chain.run(
prompt=prompt_value.to_string(),
completion=completion, def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
"""Parse the output of an LLM call using a wrapped parser.
Args:
completion: The chain completion to parse.
prompt_value: The prompt to use to parse the completion.
Returns:
The parsed completion.
"""
retries = 0 def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
retries = 0
while retries <= self.max_retries:
try:
return self.parser.parse(completion)
except OutputParserException as e:
if retries == self.max_retries:
raise
retries += 1
if self.legacy and hasattr(self.retry_chain, "run"): if self.legacy and hasattr(self.retry_chain, "run"):
completion = self.retry_chain.run(
instructions=self.parser.get_format_instructions(),
completion=completion, def parse(self, completion: str) -> T:
retries = 0
while retries <= self.max_retries:
try:
return self.parser.parse(completion)
except OutputParserException as e:
if retries == self.max_retries:
raise
retries += 1
if self.legacy and hasattr(self.retry_chain, "run"): raise ValueError(msg) from e
return evaluator_cls.from_llm(llm=llm, **kwargs)
return evaluator_cls(**kwargs)
) -> dict[str, str]:
response = self.generate([inputs], run_manager=run_manager)
return self.create_outputs(response)[0] try:
response = self.generate(input_list, run_manager=run_manager)
except BaseException as e: outputs = self.create_outputs(response)
run_manager.on_chain_end({"outputs": outputs})
return outputs
def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, str]:
response = self.generate([inputs], run_manager=run_manager)
return self.create_outputs(response)[0]
def generate(
self,
input_list: list[dict[str, Any]], def generate(
self,
input_list: list[dict[str, Any]],
run_manager: CallbackManagerForChainRun | None = None,
) -> LLMResult:
"""Generate LLM result from inputs."""
prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
callbacks = run_manager.get_child() if run_manager else None
if isinstance(self.llm, BaseLanguageModel):
return self.llm.generate_prompt(
prompts, def apply(
self,
input_list: list[dict[str, Any]],
callbacks: Callbacks = None,
) -> list[dict[str, str]]:
"""Utilize the LLM generate method for speed gains."""
callback_manager = CallbackManager.configure(
callbacks,
self.callbacks,
self.verbose,
) }
outputs = self.combine_documents_chain.run(
_inputs,
callbacks=_run_manager.get_child(), def _call(
self,
inputs: dict[str, str],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
# Split the larger text into smaller chunks.
doc_text = inputs.pop(self.input_key)
texts = self.text_splitter.split_text(doc_text)
docs = [Document(page_content=text) for text in texts]
_inputs: dict[str, Any] = { for i, chain in enumerate(self.chains):
_input = chain.run(
_input,
callbacks=_run_manager.get_child(f"step_{i + 1}"), _input = _input.strip()
_run_manager.on_text(
_input,
color=color_mapping[str(i)], def _call(
self,
inputs: dict[str, str],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, chain in enumerate(self.chains):
_input = chain.run(
_input,
return self.invoke(
inputs,
return self.invoke(
inputs,
cast("RunnableConfig", {k: v for k, v in config.items() if v is not None}), def __call__(
self,
inputs: dict[str, Any] | Any,
return_only_outputs: bool = False, # noqa: FBT001,FBT002
callbacks: Callbacks = None,
*,
tags: list[str] | None = None,
metadata: dict[str, Any] | None = None,
run_name: str | None = None,
include_run_info: bool = False,
) -> dict[str, Any]: )
def query(
self,
question: str,
llm: BaseLanguageModel | None = None, return (await chain.ainvoke({chain.input_key: question}))[chain.output_key]
def query_with_sources(
self,
question: str,
llm: BaseLanguageModel | None = None, _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
return self.llm_chain.invoke(
inputs, def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, str]:
"""Call the internal llm chain."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
return self.llm_chain.invoke(
inputs,
config={"callbacks": _run_manager.get_child()},
)
_run_manager.on_text(es_cmd, color="green", verbose=self.verbose)
intermediate_steps.append(
es_cmd, _run_manager.on_text("\nESResult: ", verbose=self.verbose)
_run_manager.on_text(result, color="yellow", verbose=self.verbose)
_run_manager.on_text("\nAnswer:", verbose=self.verbose) intermediate_steps.append(final_result) # output: final answer
_run_manager.on_text(final_result, color="green", verbose=self.verbose)
chain_result: dict[str, Any] = {self.output_key: final_result}
if self.return_intermediate_steps: def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
input_text = f"{inputs[self.input_key]}\nESQuery:"
_run_manager.on_text(input_text, verbose=self.verbose)
indices = self._list_indices()
indices_info = self._get_indices_infos(indices)
query_inputs: dict = {def create_sql_query_chain(
llm: BaseLanguageModel,
db: SQLDatabase,
prompt: BasePromptTemplate | None = None,
k: int = 5,
*,
get_col_comments: bool | None = None,
) -> Runnable[SQLInput | SQLInputWithTables | dict[str, Any], str]:
r"""Create a chain that generates SQL queries.
*Security Note*: This chain generates SQL queries for the given database.
def create_sql_query_chain(
llm: BaseLanguageModel,
db: SQLDatabase,
prompt: BasePromptTemplate | None = None, docs = self._get_docs(question) # type: ignore[call-arg]
answer = self.combine_documents_chain.run(
input_documents=docs,
question=question, def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, Any]:
"""Run get_relevant_text and llm on input query.
If chain has 'return_source_documents' as 'True', returns
the retrieved documents as well under the key 'source_documents'.
Example:
answer = self.combine_documents_chain.run(
input_documents=docs,
callbacks=_run_manager.get_child(), def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
accepts_run_manager = (
"run_manager" in inspect.signature(self._get_docs).parameters
)
if accepts_run_manager:
docs = self._get_docs(inputs, run_manager=_run_manager) def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, list]:
docs = self.text_splitter.create_documents([inputs[self.input_key]])
results = self.llm_chain.generate(
[{"text": d.page_content} for d in docs],
run_manager=run_manager,
)
qa = [json.loads(res[0].text) for res in results.generations]
_run_manager.on_text(
text="Initial response: " + response + "\n\n",
verbose=self.verbose,
raw_critique = self.critique_chain.run(
input_prompt=input_prompt,
output_from_model=response,
_run_manager.on_text(
text="Critique: " + critique + "\n\n",
verbose=self.verbose,
_run_manager.on_text(
text="Updated response: " + revision + "\n\n",
verbose=self.verbose, def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(
**inputs,
callbacks=_run_manager.get_child("original"),
)
initial_response = response return ["output"]
def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None, def _call(
self,
inputs: dict[str, str],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
url = inputs[self.input_url_key]
browser_content = inputs[self.input_browser_content_key]
llm_cmd = self.llm_chain.invoke(
{
"objective": self.objective, )
_run_manager.on_text(api_url, color="green", end="\n", verbose=self.verbose)
api_url = api_url.strip()
if self.limit_to_domains and not _check_in_allowed_domain( api_response = self.requests_wrapper.get(api_url)
_run_manager.on_text(
str(api_response),
color="yellow", _run_manager.on_text(inputs[self.input_key])
llm_output = self.llm_chain.predict(
question=inputs[self.input_key],
stop=["```output"], def _call(
self,
inputs: dict[str, str],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_run_manager.on_text(inputs[self.input_key])
llm_output = self.llm_chain.predict(
question=inputs[self.input_key],
stop=["```output"],
callbacks=_run_manager.get_child(), def _collapse_docs_func(docs: list[Document], **kwargs: Any) -> str:
return self._collapse_chain.run(
input_documents=docs,
callbacks=callbacks, def combine_docs(
self,
docs: list[Document],
callbacks: Callbacks = None,
**kwargs: Any,
) -> tuple[str, dict]:
"""Combine by mapping first chain over all, then stuffing into final chain.
Args:
docs: List of documents to combine
callbacks: Callbacks to be passed through new_inputs["chat_history"] = chat_history_str
answer = self.combine_docs_chain.run(
input_documents=docs,
callbacks=_run_manager.get_child(), context = "\n\n".join(d.page_content for d in docs)
result = self.response_chain.invoke(
{ def _do_generation(
self,
questions: list[str],
user_input: str,
response: str,
_run_manager: CallbackManagerForChainRun,
) -> tuple[str, bool]:
callbacks = _run_manager.get_child()
docs = []
for question in questions:
docs.extend(self.retriever.invoke(question)) def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
user_input = inputs[self.input_keys[0]]
response = ""
end="\n",
)
return self._do_generation(questions, user_input, response, _run_manager)
def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() return self._do_generation(questions, user_input, response, _run_manager)
def _call(
self,
inputs: dict[str, Any],
run_manager: CallbackManagerForChainRun | None = None,
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel | None,
max_generation_len: int = 32,
prediction = self.llm_chain.predict(callbacks=callbacks, **inputs)
return cast(
"dict[str, Any]", _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
raw_output = self.eval_chain.run(
chain_input,
callbacks=_run_manager.get_child(), def _call(
self,
inputs: dict[str, str],
run_manager: CallbackManagerForChainRun | None = None,
) -> dict[str, Any]:
"""Run the chain and generate the output.
Args:
inputs: The input values for the chain.
run_manager: The callback manager for the chain run.
def create_openai_tools_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
def create_openai_functions_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
def create_tool_calling_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
def create_json_chat_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
def create_xml_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: BasePromptTemplate, elif "run_id" not in input:
_ = self.client.beta.threads.messages.create(
input["thread_id"], }
return self.client.beta.threads.runs.create(
input_dict["thread_id"], run = self.client.beta.threads.runs.submit_tool_outputs(**input)
run = self._wait_for_run(run.id, run.thread_id)
except BaseException as e:
run_manager.on_chain_error(e) else:
run_manager.on_chain_end(response)
return response
except BaseException as e:
run_manager.on_chain_error(e, metadata=run.dict())
raise
else: def invoke(
self,
input: dict,
config: RunnableConfig | None = None,
**kwargs: Any,
) -> OutputType:
"""Invoke assistant.
Args:
input: Runnable input dict that can have:
content: User message when starting a new run. def _create_run(self, input_dict: dict) -> Any:
params = {
k: v
for k, v in input_dict.items()
if k
in (
"instructions",
"model",
"tools",
"additional_instructions",
"parallel_tool_calls", def _wait_for_run(self, run_id: str, thread_id: str) -> Any:
in_progress = True
while in_progress:
run = self.client.beta.threads.runs.retrieve(run_id, thread_id=thread_id)
in_progress = run.status in ("in_progress", "queued")
if in_progress:
sleep(self.check_every_ms / 1000)
return run
async def _aparse_intermediate_steps(
self,
@override
def invoke(
self,
input: dict,
config: RunnableConfig | None = None,
def create_react_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: BasePromptTemplate, client = client or Client()
container = _DatasetRunContainer.prepare(
client,
dataset_name, batch_results = [
_run_llm_or_chain(
example,
config, else:
with runnable_config.get_executor_for_config(container.configs[0]) as executor:
batch_results = list(
executor.map( batch_results = list(
executor.map(
functools.partial(
_run_llm_or_chain,def _run_llm(
llm: BaseLanguageModel,
inputs: dict[str, Any],
callbacks: Callbacks,
*,
tags: list[str] | None = None,
input_mapper: Callable[[dict], Any] | None = None,
metadata: dict[str, Any] | None = None,
) -> str | BaseMessage:
"""Run the language model on the example.
## Sync Utilities
def _run_llm(
llm: BaseLanguageModel,
inputs: dict[str, Any],
callbacks: Callbacks,
*,
tags: list[str] | None = None,
def _run_llm(
llm: BaseLanguageModel,
inputs: dict[str, Any],
callbacks: Callbacks,
def run_on_dataset(
client: Client | None,
dataset_name: str,
llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, def _prepare_input(self, inputs: dict[str, Any]) -> dict[str, str]:
run: Run = inputs["run"]
example: Example | None = inputs.get("example")
evaluate_strings_inputs = self.run_mapper(run)
if not self.string_evaluator.requires_input:
# Hide warning about unused input
evaluate_strings_inputs.pop("input", None)
if example and self.example_mapper and self.string_evaluator.requires_reference:
evaluate_strings_inputs.update(self.example_mapper(example))
elif self.string_evaluator.requires_reference:
msg = ( return ["feedback"]
def _prepare_input(self, inputs: dict[str, Any]) -> dict[str, str]:
run: Run = inputs["run"]
example: Example | None = inputs.get("example")
evaluate_strings_inputs = self.run_mapper(run) """Filter down documents based on their relevance to the query."""
results = self.reranker.invoke(
{"documents": documents, "query": query}, def compress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Callbacks | None = None,
) -> Sequence[Document]:
"""Filter down documents based on their relevance to the query."""
results = self.reranker.invoke(
{"documents": documents, "query": query},
config={"callbacks": callbacks},
) def compress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Callbacks | None = None,
) -> Sequence[Document]:
"""Rerank documents using CrossEncoder.
Args:
documents: A sequence of documents to compress.
query: The query to use for compressing the documents. def compress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Callbacks | None = None,
) -> Sequence[Document]:
"""Compress page content of raw documents."""
compressed_docs = []
for doc in documents:
_input = self.get_input(query, doc)
output_ = self.llm_chain.invoke(_input, config={"callbacks": callbacks}) ) -> list[Document]:
structured_query = self.query_constructor.invoke(
{"query": query}, def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> list[Document]:
structured_query = self.query_constructor.invoke(
{"query": query},
config={"callbacks": run_manager.get_child()},
)
if self.verbose: "ChatGeneration",
self.generate_prompt(
[self._convert_input(input)], "AIMessageChunk",
self.invoke(input, config=config, stop=stop, **kwargs),
) ) -> Iterator[str]:
result = self.invoke(input, config)
for i_c, c in enumerate(result): def stream(
self,
input: LanguageModelInput,
config: RunnableConfig | None = None,
*,
stop: list[str] | None = None,
**kwargs: Any,
) -> Iterator[str]:
result = self.invoke(input, config)
for i_c, c in enumerate(result):
if self.sleep is not None: return (
self.generate_prompt(
[self._convert_input(input)], try:
llm_result = self.generate_prompt(
[self._convert_input(input_) for input_ in inputs], except RuntimeError:
asyncio.run(coro)
else:
if loop.is_running(): else:
asyncio.run(coro)
except Exception as e:
_log_error_once(f"Error in on_retry: {e}") def invoke(
self,
input: LanguageModelInput,
config: RunnableConfig | None = None,
*,
stop: list[str] | None = None,
**kwargs: Any,
) -> str:
config = ensure_config(config)
return (
self.generate_prompt( def stream(
self,
input: LanguageModelInput,
config: RunnableConfig | None = None,
*,
stop: list[str] | None = None,
**kwargs: Any,
) -> Iterator[str]:
if type(self)._stream == BaseLLM._stream: # noqa: SLF001
# model doesn't implement streaming, so use default implementation
yield self.invoke(input, config=config, stop=stop, **kwargs) def batch(
self,
inputs: list[Any],
config: RunnableConfig | list[RunnableConfig] | None = None,
*,
return_exceptions: bool = False,
**kwargs: Any,
) -> list[AIMessage]:
if isinstance(config, list):
return [
self.invoke(m, c, **kwargs) ) -> str | tuple[str, list[Document]]:
docs = retriever.invoke(query, config={"callbacks": callbacks})
content = document_separator.join( def func(
query: str, callbacks: Callbacks = None
) -> str | tuple[str, list[Document]]:
docs = retriever.invoke(query, config={"callbacks": callbacks})
content = document_separator.join(
format_document(doc, document_prompt_) for doc in docs
)
if response_format == "content_and_artifact":
return (content, docs)
return content
output = _format_output(content, artifact, tool_call_id, self.name, status)
run_manager.on_tool_end(output, color=color, name=self.name, **kwargs)
return output
if error_to_raise:
run_manager.on_tool_error(error_to_raise, tool_call_id=tool_call_id)
raise error_to_raise
output = _format_output(content, artifact, tool_call_id, self.name, status) def invoke(
self,
input: str | dict | ToolCall,
config: RunnableConfig | None = None,
**kwargs: Any,
) -> Any:
tool_input, kwargs = _prep_run_args(input, config, **kwargs)
return self.run(tool_input, **kwargs)
@override
async def ainvoke( while pending := asyncio.all_tasks(runner.get_loop()):
runner.run(asyncio.wait(pending))
else:
# Before Python 3.11 we need to run each coroutine in a new event loop try:
asyncio.run(coro)
except Exception as e:
logger.warning("Error in callback coroutine: %s", repr(e)) try:
runner.run(coro)
except Exception as e:
logger.warning("Error in callback coroutine: %s", repr(e))def _run_coros(coros: list[Coroutine[Any, Any, Any]]) -> None:
if hasattr(asyncio, "Runner"):
# Python 3.11+
# Run the coroutines in a new event loop, taking care to
# - install signal handlers
# - run pending tasks scheduled by `coros`
# - close asyncgens and executors
# - close the loop
with asyncio.Runner() as runner:
# Run the coroutine, get the result
for coro in coros: if draw_method == MermaidDrawMethod.PYPPETEER:
img_bytes = asyncio.run(
_render_mermaid_using_pyppeteer(
mermaid_syntax, output_file_path, background_color, padding ctx = copy_context()
config_token, _ = ctx.run(_set_config_context, config)
try:
yield ctx finally:
ctx.run(var_child_runnable_config.reset, config_token)
ctx.run(
_set_tracing_context, ctx.run(var_child_runnable_config.reset, config_token)
ctx.run(
_set_tracing_context,
{ def _wrapped_fn(*args: Any) -> T:
return contexts.pop().run(fn, *args)
return super().map(def _set_config_context(
config: RunnableConfig,
) -> tuple[Token[RunnableConfig | None], dict[str, Any] | None]:
"""Set the child Runnable config + tracing context.
Args:
config: The config to set.
Returns:
The token to reset the config and the previous tracing context.
""" else:
return bound.invoke(input_, config, **kwargs)
try:
return bound.invoke(input_, config, **kwargs)
except Exception as e: def invoke(
self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
) -> Output:
runnable, config = self.prepare(config)
return runnable.invoke(input, config, **kwargs)
@override
async def ainvoke(
self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
) -> Output:
runnable, config = self.prepare(config) def invoke(
prepared: tuple[Runnable[Input, Output], RunnableConfig],
input_: Input,
) -> Output | Exception:
bound, config = prepared
if return_exceptions:
try:
return bound.invoke(input_, config, **kwargs)
except Exception as e:
return e
else:
expression_value = condition.invoke(
input, else:
output = self.default.invoke(
input, if expression_value:
output = runnable.invoke(
input,
expression_value = condition.invoke(
input, raise
run_manager.on_chain_end(output)
return output
def invoke(
self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
) -> Output:
"""First evaluates the condition, then delegate to `True` or `False` branch.
Args:
input: The input to the `Runnable`.
config: The configuration for the `Runnable`.
**kwargs: Additional keyword arguments to pass to the `Runnable`.
Returns: def stream(
self,
input: Input,
config: RunnableConfig | None = None,
**kwargs: Any | None,
) -> Iterator[Output]:
"""First evaluates the condition, then delegate to `True` or `False` branch.
Args:
input: The input to the `Runnable`.
config: The configuration for the `Runnable`. with attempt:
result = super().invoke(
input_, def _invoke(
self,
input_: Input,
run_manager: "CallbackManagerForChainRun",
config: RunnableConfig,
**kwargs: Any,
) -> Output:
for attempt in self._sync_retrying(reraise=True):
with attempt:
result = super().invoke(
input_, with set_config_context(child_config) as context:
output = context.run(
runnable.invoke, with set_config_context(child_config) as context:
stream = context.run(
runnable.stream, else:
run_manager.on_chain_end(output)
return output
if first_error is None: )
chunk: Output = context.run(next, stream)
except self.exceptions_to_handle as e:
first_error = e if first_error is None else first_error def stream(
self,
input: Input,
config: RunnableConfig | None = None,
**kwargs: Any | None,
) -> Iterator[Output]:
if self.exception_key is not None and not isinstance(input, dict):
msg = (
"If 'exception_key' is specified then input must be a dictionary."
f"However found a type of {type(input)} for input"
) else:
return runnable.invoke(input_, config, **kwargs)
try:
return runnable.invoke(input_, config, **kwargs)
except Exception as e: def invoke(
self, input: RouterInput, config: RunnableConfig | None = None, **kwargs: Any
) -> Output:
key = input["key"]
actual_input = input["input"]
if key not in self.runnables:
msg = f"No runnable associated with key '{key}'"
raise ValueError(msg)
runnable = self.runnables[key]
return runnable.invoke(actual_input, config) def invoke(
runnable: Runnable[Input, Output], input_: Input, config: RunnableConfig
) -> Output | Exception:
if return_exceptions:
try:
return runnable.invoke(input_, config, **kwargs)
except Exception as e:
return e
else:
return runnable.invoke(input_, config, **kwargs)
"Output",
context.run(
call_func_with_variable_args, # type: ignore[arg-type] else:
out = self.invoke(input_, config, **kwargs)
try:
out: Output | Exception = self.invoke(input_, config, **kwargs)
except Exception as e: with set_config_context(child_config) as context:
return context.run(
step.invoke, while True:
chunk: Output = context.run(next, iterator)
yield chunk
if final_output_supported: def stream(
self,
input: Input,
config: RunnableConfig | None = None,
**kwargs: Any | None,
) -> Iterator[Output]:
"""Default implementation of `stream`, which calls `invoke`.
Subclasses must override this method if they support streaming output.
Args: def _call_with_config(
self,
func: Callable[[Input], Output]
| Callable[[Input, CallbackManagerForChainRun], Output]
| Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output],
input_: Input,
config: RunnableConfig | None,
run_type: str | None = None,
serialized: dict[str, Any] | None = None,
**kwargs: Any | None,
) -> Output: def _transform_stream_with_config(
self,
inputs: Iterator[Input],
transformer: Callable[[Iterator[Input]], Iterator[Output]]
| Callable[[Iterator[Input], CallbackManagerForChainRun], Iterator[Output]]
| Callable[
[Iterator[Input], CallbackManagerForChainRun, RunnableConfig],
Iterator[Output],
],
config: RunnableConfig | None,
run_type: str | None = None, def invoke(
self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
) -> Output:
# setup callbacks and context
config = ensure_config(config)
callback_manager = get_callback_manager_for_config(config)
# start the root run
run_manager = callback_manager.on_chain_start(
None,
input,
name=config.get("run_name") or self.get_name(), def invoke(
self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
) -> dict[str, Any]:
# setup callbacks
config = ensure_config(config)
callback_manager = CallbackManager.configure(
inheritable_callbacks=config.get("callbacks"),
local_callbacks=None,
verbose=False,
inheritable_tags=config.get("tags"),
local_tags=None, def invoke(
self,
input: Input,
config: RunnableConfig | None = None,
**kwargs: Any | None,
) -> Output:
return self.bound.invoke(
input,
self._merge_configs(config),
**{**self.kwargs, **kwargs},
) def invoke(input_: Input, config: RunnableConfig) -> Output | Exception:
if return_exceptions:
try:
return self.invoke(input_, config, **kwargs)
except Exception as e:
return e
else:
return self.invoke(input_, config, **kwargs)
# If there's only one input, don't bother with the executor
if len(inputs) == 1: def invoke(
i: int, input_: Input, config: RunnableConfig
) -> tuple[int, Output | Exception]:
if return_exceptions:
try:
out: Output | Exception = self.invoke(input_, config, **kwargs)
except Exception as e:
out = e
else:
out = self.invoke(input_, config, **kwargs)
def _invoke_step(
step: Runnable[Input, Any], input_: Input, config: RunnableConfig, key: str
) -> Any:
child_config = patch_config(
config,
# mark each step as a child run
callbacks=run_manager.get_child(f"map:key:{key}"),
)
with set_config_context(child_config) as context:
return context.run(
step.invoke, run.dotted_order += "." + current_dotted_order
if parent_run := self.run_map.get(str(run.parent_run_id)):
self._add_child_run(parent_run, run)
else: while current_run.parent_run_id:
parent = self.run_map.get(str(current_run.parent_run_id))
if parent:
parents.append(parent) run_type = run.run_type.capitalize()
self.function_callback(
f"{get_colored_text('[chain/start]', color='green')} "
+ get_bolded_text(f"[{crumbs}] Entering {run_type} run with input:\n") run_type = run.run_type.capitalize()
self.function_callback(
f"{get_colored_text('[chain/end]', color='blue')} "
+ get_bolded_text( run_type = run.run_type.capitalize()
self.function_callback(
f"{get_colored_text('[chain/error]', color='red')} "
+ get_bolded_text( def get_parents(self, run: Run) -> list[Run]:
"""Get the parents of a run.
Args:
run: The run to get the parents of.
Returns:
A list of parent runs.
"""
parents = []
current_run = run self._persist_run(run)
self.run_map.pop(str(run.id))
self._on_run_update(run)
self._on_run_create(run)
def _end_trace(self, run: Run) -> None:
"""End a trace for a run."""
if not run.parent_run_id:
self._persist_run(run)Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'ConfigAnalyzer' object has no attribute 'file_exists'
Implement statistical analysis on prompt patterns
Use ML-based anomaly detection for unusual inputs
Set up alerts for prompt anomaly detection
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Implement watermarking for model outputs
Use cryptographic watermarks for model weights
Track watermark verification for model theft detection
Detection failed: 'bool' object has no attribute 'lower'
Use Presidio or SpaCy for NER-based PII detection
Implement custom NER models for domain-specific PII
Run PII detection on all inputs and outputs
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Implement rate limiting on API endpoints
Add query logging and anomaly detection
Monitor for extraction patterns
Implement drift detection with evidently or alibi-detect
Monitor input data distribution changes
Set up automated alerts for drift events
Implement anomaly detection on model inputs
Monitor for unusual query patterns
Use statistical methods or ML-based detection
Implement adversarial input detection
Use adversarial robustness toolkits
Add input perturbation analysis
Detection failed: 'bool' object has no attribute 'lower'
Use Evidently or alibi-detect for drift monitoring
Set up automated alerts for significant drift
Implement automatic retraining pipelines
Use SHAP or LIME for model explanations
Provide decision explanations in outputs
Implement feature attribution tracking
Use Fairlearn or AIF360 for bias detection
Implement fairness metrics tracking
Test for demographic parity and equalized odds
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Use MLflow, DVC, or Weights & Biases for model tracking
Implement model versioning with metadata
Maintain model registry with provenance information
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Use Fairlearn or AIF360 for fairness metrics
Implement demographic parity testing
Monitor fairness metrics in production
Implement adversarial testing for bias
Test across demographic groups
Use TextAttack or CheckList for NLP bias testing
Detection failed: 'ConfigAnalyzer' object has no attribute 'file_exists'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Code Execution: 1. Never pass LLM output to eval() or exec() 2. Use safe alternatives (ast.literal_eval for data) 3. Implement sandboxing if code execution is required
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Code Execution: 1. Never pass LLM output to eval() or exec() 2. Use safe alternatives (ast.literal_eval for data) 3. Implement sandboxing if code execution is required
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Secure Tool/Plugin Implementation: 1. NEVER execute shell commands from LLM output directly 2. Use allowlists for permitted commands/operations 3. Validate all file paths against allowed directories 4. Use parameterized queries - never raw SQL from LLM 5. Validate URLs against allowlist before HTTP requests 6. Implement strict input schemas (JSON Schema, Pydantic) 7. Add rate limiting and request throttling 8. Log all tool invocations for audit 9. Use principle of least privilege 10. Implement human-in-the-loop for destructive operations
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security, data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards