Parameter Provenance Validator

Software componentSafety & SecuritySafety, Security & Governancearc:ParameterProvenanceValidator

A pre-execution validation gate that traces each tool parameter value to a legitimate source (user input, authenticated session, retrieved context or prior tool output) and blocks untraceable or low-confidence values.

Responsibility. Blocks hallucinated tool parameter values that cannot be traced to trusted sources.

Also known as: Semantic Parameter Validator, Grounding checks, Parameter hallucination detection, Reliability alignment

Direct neighbourhood (hover for relationship types)

Relationships

escalates to dynamic

guards control

Tool Executor Ch3.8
Tool Integration Adapter abstract Ch3.7

Design guidance

MUST NOT accept security-critical parameters (user IDs, tokens, account numbers) from external input without validation against authenticated session context.
SHOULD block or request confirmation for parameters with unknown source before critical actions.
MUST flag parameter values not grounded in evidence for additional verification or user clarification before execution.
SHOULD require high-stakes values (e.g., shipping addresses) to be explicitly extracted from customer data or confirmed via explicit tool calls, not inferred.

Quantitative guidance

As stated by the sources; verify before use.

Layered static, semantic, security and grounding validation catches 85-95% of parameter errors before execution (Ch3.8).
Without grounding checks agents fabricate parameters in 15-30% of tool invocations depending on task complexity; grounding verification reduces this to 2-5% (research cited in Ch3.7).

Classification

Patterns: Parameter provenance trackingConfidence-gated confirmation for inferred valuesReliability alignment (verify parameters are grounded in context)Semantic validation
Quality attributes: Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Safety (ISO/IEC 25010 | NIST AI RMF: safe)
Risks mitigated: Parameter hallucinationPrompt-manipulated parameter injectionFabricated identifiers (order, user, flight numbers)Inferred rather than explicitly extracted shipping addresses

Sources

Ch3.7: T. Nguyen, "Tool Usage Auditing," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.7. ISBN: 9798244538229.
Ch3.8: T. Nguyen, "Action Accuracy Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.8. ISBN: 9798244538229.