Lacking ground truth and faced with ambiguous prompts or helpfulness-biased tuning, the model fabricates plausible but false facts, figures, or citations, presented with high fluency that makes errors hard to catch and likely to be acted upon.
AI/ML / Multi Agent Refarch / Threats / DEV
Confident hallucination and fabricated facts
CCC.MARefArc.TH16
Related Capabilities
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.CP16 | Model-interaction zero-trust guardrails | Enforces authentication and authorization for every inference request and applies input validation against prompt injection, output filtering and redaction, access control, rate limits, and cost management before and after model execution. |
| CCC.MARefArc.CP22 | Runtime protection | Monitors agent actions and model outputs during execution to detect unsafe, non-compliant, or anomalous behavior, enforcing constraints, blocking disallowed actions, or triggering escalation. |
| CCC.MARefArc.CP02 | Human-in-the-loop output review | Application-embedded controls that allow users to review, approve, or modify agent outputs before they are executed or shared. |
Related Controls
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.CN03 | System Acceptance Testing | Validate agents, models, and end-to-end workflows against accuracy, robustness, bias, drift, and compliance criteria before promotion to production, and re-validate after material changes. |
| CCC.MARefArc.CN04 | Data Quality and Classification | Assess the quality of, and assign classification and sensitivity labels to, all data used for grounding, training, and fine-tuning, and enforce handling rules derived from those labels throughout the Knowledge and LLM layers. |
| CCC.MARefArc.CN20 | Citations and Source Traceability for AI-Generated Information | Attach citations and source traceability to AI-generated information so that outputs can be verified against retrieved sources and decisions can be explained. |
| CCC.MARefArc.CN21 | Automated Evaluation Using LLM-as-a-Judge | Use automated model-based evaluation in the Evaluation Layer to assess output quality, grounding, bias, and policy compliance at scale. |
External Mappings
| Framework | ID | Remarks |
|---|---|---|
| air-vec | AIR-OP-004-01 | |
| air-vec | AIR-OP-004-02 | |
| air-vec | AIR-OP-004-03 | |
| air-vec | AIR-OP-004-04 |