| name | ai-mcp-audit |
| description | Audits MCP servers and skills on demand using LLM coherence analysis to catch capability drift and rug-pulls. Trigger for 'audit this skill', 'is this MCP safe', 'check coherence', 'detect rug-pull', 'snapshot baseline', 'mcp audit'. Three modes: scan (declared-vs-observed), audit-update (post-update diff), baseline set (anchor known-good). Not for runtime payload inspection; use prompt-injection-guard hook instead. Not for CVE scanning; use /ai-security instead. |
| effort | high |
| argument-hint | scan|audit-update [skill]|baseline set [--target skill-or-all] |
| tags | ["security","mcp","audit","governance"] |
| model_tier | opus |
| mirror_family | antigravity-skills |
| generated_by | ai-eng sync |
| canonical_source | .claude/skills/ai-mcp-audit/SKILL.md |
| edit_policy | generated-do-not-edit |
MCP Audit — On-Demand Skill & MCP Server Security Audit
Workflow
Cold-path LLM-driven security audit (spec-107 D-107-08). Principles applied: §10.1 KISS
(the deterministic $0 hot path stays separate from the on-demand LLM cold path — no single
over-complex gate straddling both threat models).
Two tiers:
| Tier | Mechanism | Cost | Role |
|---|
| Hot path (Capa 1) | prompt-injection-guard.py PreToolUse hook — deterministic IOC matching, injection-immune (D-107-06) | $0 | runtime payload control |
| Cold path (Capa 2, this skill) | on-demand LLM analysis | per-run (confirm first) | post-install review, pre-merge audit |
Three modes:
- scan — coherence: declared
description vs observed code behavior.
- audit-update — rug-pull: diff post-update files against the trusted baseline.
- baseline set — anchor a tamper-evident reference for future audits.
Does NOT replace /ai-security (CVE/SBOM), /ai-governance (compliance), or /ai-verify (quality).
Modes
Mode 1 — scan (coherence analysis)
ai-mcp-audit scan [--target <path-or-skill-name>]
- LLM compares declared
description vs actual code (handlers, scripts, references).
- Per surface: GREEN (VERDE) = coherent, or RED (ROJO) = suspicious (capability creep, malicious injection, rug-pull).
- Output: structured JSON at
.ai-engineering/state/sentinel-scan-report.json + human-readable stdout.
--target scopes to a single surface (cost-saving). Cost estimate shown pre-run; user must confirm.
Mode 2 — audit-update <skill> (rug-pull detection)
ai-mcp-audit audit-update <skill-name>
- Reads baseline from
.ai-engineering/state/sentinel-baseline.json (Mode 3 must run first; without it, errors with a hint pointing to Mode 3).
- Walks current files + computes semantic-capability diff: new network calls, file accesses outside scope, env reads, subprocess invocations, SKILL.md frontmatter capability claims.
- Each delta: severity HIGH/MEDIUM/LOW + exact diff + remediation hint.
- Postmark-class threat detection: silent semantic drift that bypasses CVE/SBOM scanning.
Mode 3 — baseline set (anchoring)
ai-mcp-audit baseline set [--target <skill-name>|all]
- Anchors a snapshot to
.ai-engineering/state/sentinel-baseline.json.
- Per skill: SHA256 of every file + extracted capabilities (network/file/env/subprocess + frontmatter claims).
--target all regenerates the entire baseline (confirmation prompt). Without a baseline, Mode 2 errors.- Canonical-JSON
sort_keys=True for stability; candidate for H2 hash-chained audit trail (D-107-10).
Non-Goals
- No automatic invocation (Q6-3B + OQ-2) — cold-path on-demand only.
- No remote MCP server analysis (OQ-4) — local-only in spec-107.
- No auto-fix of flagged skills.
- No replacement for
/ai-security (different threat models).
State Files
.ai-engineering/state/sentinel-baseline.json — trusted snapshot (Mode 3 writes; Mode 2 reads)..ai-engineering/state/sentinel-scan-report.json — most recent Mode 1 output..ai-engineering/state/decision-store.json — risk-acceptance entries for accepted ROJO verdicts (sentinel-coherence-<skill> finding-id, spec-105 lifecycle).
Integration
Complements /ai-security (CVE/SBOM/secrets) with coherence/rug-pull; feeds VERDE/ROJO verdicts to /ai-governance; pairs with /ai-ide-audit (platform vs. behavior). Cold-path counterpart to the prompt-injection-guard hot-path hook (D-107-06).
Examples
Example — coherence scan after installing a new skill
User: "I just installed a new skill from a third-party repo, audit it"
/ai-mcp-audit scan --target ai-foo-bar
Runs LLM coherence analysis comparing the declared description against actual handler code; emits VERDE / ROJO per surface; ROJO triggers the risk-acceptance flow.
References
.ai-engineering/specs/spec.md — D-107-08 design rationale..ai-engineering/scripts/hooks/prompt-injection-guard.py — hot-path runtime counterpart..ai-engineering/security/iocs/IOCS_ATTRIBUTION.md — IOC catalog provenance..ai-engineering/reference/mcp-binary-policy.md — MCP binary allowlist policy (spec-107).
