Generated: 2026-01-12 12:40:27 UTC
def sync_main() -> None:
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
token_provider: AzureADTokenProvider = get_bearer_token_provider(DefaultAzureCredential(), scopes) )
return self.runs.poll(run.id, run.thread_id, extra_headers, extra_query, extra_body, timeout, poll_interval_ms) # pyright: ignore[reportDeprecated]
@overload stream=stream or False,
stream_cls=Stream[AssistantStreamEvent],
)
def create_and_run_poll(
self,
*,
assistant_id: str,
instructions: Optional[str] | Omit = omit,
max_completion_tokens: Optional[int] | Omit = omit, )
return self.runs.poll(run.id, run.thread_id, extra_headers, extra_query, extra_body, timeout, poll_interval_ms) # pyright: ignore[reportDeprecated]
@overload
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | Omit = omit,
max_completion_tokens: Optional[int] | Omit = omit, """Create a thread and stream the run back"""
...
@overload
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | Omit = omit,
max_completion_tokens: Optional[int] | Omit = omit, ) -> AssistantStreamManager[AssistantEventHandlerT]:
"""Create a thread and stream the run back"""
...
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | Omit = omit,
max_completion_tokens: Optional[int] | Omit = omit, run.id, run.thread_id, extra_headers, extra_query, extra_body, timeout, poll_interval_ms
)
@overload
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | Omit = omit,
max_completion_tokens: Optional[int] | Omit = omit, """Create a thread and stream the run back"""
...
@overload
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | Omit = omit,
max_completion_tokens: Optional[int] | Omit = omit, ) -> AsyncAssistantStreamManager[AsyncAssistantEventHandlerT]:
"""Create a thread and stream the run back"""
...
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | Omit = omit,
max_completion_tokens: Optional[int] | Omit = omit,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'
Use Opacus or TensorFlow Privacy for differential privacy
Implement privacy budgets for model queries
Monitor epsilon values for privacy guarantees
Implement watermarking for model outputs
Use cryptographic watermarks for model weights
Track watermark verification for model theft detection
Use safetensors instead of pickle for model weights
Set weights_only=True when using torch.load
Validate model files before loading
Use Presidio or similar for PII detection
Implement NER-based PII detection with spaCy
Add custom regex patterns for domain-specific PII
Implement data masking for sensitive fields
Use tokenization for reversible anonymization
Apply redaction before logging or storage
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'
Implement data validation pipelines
Verify data source integrity
Monitor for anomalies in training data
Detection failed: 'bool' object has no attribute 'lower'
Implement PII detection and filtering
Never include secrets in prompts
Add output filtering for sensitive patterns
Implement human-in-the-loop for critical actions
Use principle of least privilege for LLM access
Add approval workflows for sensitive operations
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'
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
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
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
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
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
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
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
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 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