AI Security Atlas › All entries › LLM Application & Output Security
Uncertainty Labeling & High-Stakes Review
AI output is labeled, limits are stated where users decide, and answers in high-stakes categories go to a trained reviewer.
Open in the interactive atlas →Impact
Keeps people from acting on unverified output where errors are costly.
Why this severity
Guardrails take the highest severity of the risks they mitigate: Interface Invites Overreliance (Medium), Hallucinated Answers Acted On (High).
Method: OWASP Risk Rating Methodology
Related
Mitigates
- Interface Invites Overreliance Vulnerability · Medium severity
- Hallucinated Answers Acted On Threat · High severity
Builds on
- Grounding & Citation Verification Guardrail · Fixes high risks
Remediation
- Classify questions by stakes and route the top tier to human review.
- Show sources and an AI-generated label on every answer.
- Train staff on the system's known failure modes.
How to verify
- Review queue SLAs are met for high-stakes categories.
- Audit a sample of auto-answered questions for misrouted high-stakes items.
Framework mapping
OWASP Top 10 for LLM Applications 2025
- LLM09:2025 Misinformation
OWASP Top 10 for Agentic Applications 2026
- ASI09:2026 Human-Agent Trust Exploitation
MITRE ATLAS
- AML.M0018 User Training
NIST AI RMF 1.0
- MEASURE 2.9 Model is explained and output is interpreted within its context
- MANAGE 4.1 Post-deployment monitoring, incident response, and change management