# Overreliance

> Detects systems that treat LLM output as authoritative fact in consequential decisions without human review. Use when writing code that displays LLM output as authoritative fact, uses LLM decisions to gate consequential outcomes without human review, or builds automated pipelines where LLM judgment drives downstream actions.

- Skill: `thejefflarson/overreliance` (Agent Skill)
- Install (CLI): `npx skillmds@latest add thejefflarson/overreliance`
- Raw SKILL.md: https://api.skillmd.com/api/skills/thejefflarson/overreliance/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: thejefflarson (https://skillmd.com/u/thejefflarson)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/thejefflarson/overreliance

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# Overreliance on LLM Output (OWASP LLM09:2025)

## What this checks

Prevents systems from treating LLM output as ground truth. LLMs hallucinate, produce
confident-sounding errors, and lack real-time knowledge. Acting on unverified output
in medical, legal, financial, or deployment contexts can cause serious harm.

## Vulnerable patterns

- LLM diagnosis, legal advice, or financial guidance displayed in the UI with no caveat or disclaimer.
- Automated pipeline that merges, deploys, or publishes based solely on an LLM approval signal.
- Confidence threshold defined as a constant but never used to branch behavior — every non-null response is accepted.
- High-stakes domain list defined but never compared against the current request before action.
- No alternate path when the LLM output fails a sanity check or confidence threshold.

## Fix immediately

Flag the vulnerable code and explain the risk. Then suggest a fix that establishes
these properties. Translate each property into the audited file's language and
framework — apply the principles with whatever conditional, logging, and
routing primitives the host stack provides.

1. **Gate on confidence and domain, and the gate must branch.** Defining a
   confidence threshold or a high-stakes domain set without a conditional that
   actually diverges behavior (review queue versus direct return, proceed
   versus halt) is the exact bug this skill prevents. The failing branch routes
   to human review; the passing branch attaches a disclaimer and returns.
2. **No raw model output reaches the caller.** Every return site wraps the
   content with an "AI-generated — verify before acting" disclaimer or
   equivalent marker.
3. **Irreversible actions (merge, deploy, payment, publish) require a human
   trigger** — they are never invoked from the function that consumes the LLM
   result.
4. **The audit log captures enough context to reconstruct the decision**: the
   inputs the LLM saw, the output it produced, and the confidence signal.
   Metadata alone (request id, timestamp, domain) is insufficient — a reviewer
   cannot second-guess a decision they cannot re-read.

## Verification

Confirm these properties hold (language-agnostic; apply only where the pattern is
present):

- [ ] Every return site that emits LLM-generated content attaches a disclaimer or equivalent "AI-generated" marker — no path emits raw model output
- [ ] A confidence signal is compared against a named threshold in a conditional whose branches diverge (human-review path vs. direct return, or halt vs. proceed). Threshold constants that are defined but never branched on do not satisfy this
- [ ] High-stakes domains are checked against an explicit list before any automated action, and the failing branch routes to human review rather than returning LLM output
- [ ] Irreversible downstream actions (merge, deploy, payment, publish) are invoked only from a function separate from the one consuming the LLM result
- [ ] Audit-log call sites record sufficient context to reconstruct the decision — at minimum the LLM's input, its output, and the confidence signal. Logging only metadata is not enough

## References

- CWE-1021 ([Improper Restriction of Rendered UI Layers](https://cwe.mitre.org/data/definitions/1021.html))
- [OWASP LLM09:2025 Overreliance](https://genai.owasp.org/llmrisk/llm09-overreliance-on-llm-output/)