Prompt Injection Auditor Pro

Enterprise-grade security auditing: batch scan your entire skill library, map findings to OWASP LLM Top 10, generate SARIF output for GitHub Code Scanning, and auto-generate remediation patches.

Free vs Pro

The free auditor scans SKILL.md files and agent configs for known risk patterns. Pro adds depth: multi-turn escalation scenarios, encoding-bypass detection, tool-permission graph analysis, per-skill risk scoring with trend tracking across versions, and batch scanning of an entire skill library with a single report. Free checks one file — Pro audits the library.

Upgrade Path

Free for vetting a skill before install; Pro when you're curating a library or publishing skills yourself.

The Problem

You've installed 30 AI agent skills from various sources. Each one has a SKILL.md file with instructions the agent will follow. One skill tells the agent to run rm -rf {{directory}} with user-provided input — a direct shell injection. Another hardcodes an API key in its instructions. A third gives the agent unrestricted filesystem access without any path validation. You have no way to systematically audit these skills for security vulnerabilities, and manually reading 30 SKILL.md files is not a scalable security practice.

What You Get

• 12-category threat detection engine — scans for direct injection, secret exposure, over-permissive access, data exfiltration, supply chain attacks, prompt leaking, infinite recursion, privilege escalation, context window abuse, multi-agent confusion, tool chain poisoning, and compliance violations
• OWASP LLM Top 10 mapping — every finding maps to a specific OWASP identifier (LLM01 through LLM10) with a risk score on a 0-10 CVSS-like scale using attack vector, complexity, privileges, and impact multipliers
• Batch directory scanning — recursively discovers all SKILL.md, CLAUDE.md, and AGENTS.md files, applies parallel scanning with thread pool, and generates per-file results with an aggregate summary
• SARIF output — produces standard Static Analysis Results Interchange Format output that uploads directly to GitHub Code Scanning for PR-level security annotations
• Custom rule engine — define organization-specific security rules in YAML with regex patterns, severity overrides, and OWASP mappings that merge with the built-in 12-category rules
• Auto-remediation patches — generates concrete code fixes for each finding (e.g., replacing rm -rf {{directory}} with an allowlist-validated path) with before/after diffs

Why This Beats Prompting It Yourself

Reading 30 SKILL.md files manually and asking "is this secure?" produces an unreliable assessment because you don't have the complete threat taxonomy memorized. A prompt-based review catches obvious issues (hardcoded passwords) but misses subtle ones (supply chain attacks via dependency confusion, context window abuse via token flooding, multi-agent confusion via impersonation). The 12-category engine covers attack vectors that even experienced security engineers would miss in a manual review of agent instructions.

Example Session

You: Audit all skills in ~/.claude/skills/ for security issues.

Agent: Scanned 23 SKILL.md files across 4 agent frameworks. Overall Risk Score: 4.7/10 (MEDIUM). 14 findings across 6 files. | Finding | OWASP | Score | File | |---------|-------|-------|------| | Direct shell injection (rm -rf) | LLM01 | 9.2 CRITICAL | deploy-helper/SKILL.md:12 | | Hardcoded API key | LLM02 | 7.8 HIGH | analytics-tracker/SKILL.md:8 | | Unrestricted filesystem access | LLM06 | 6.4 HIGH | file-manager/SKILL.md:23 | | External HTTP call without validation | LLM07 | 5.1 MEDIUM | api-connector/SKILL.md:45 |

Auto-remediation generated for all 14 findings. SARIF output ready for upload: skill-audit-results.sarif.

Use Cases

• Auditing a library of 20+ AI agent skills before deploying them to production environments
• Scanning CLAUDE.md and AGENTS.md files in a repository for embedded security vulnerabilities
• Integrating skill security scanning into CI/CD pipelines to block new vulnerable skills from being merged
• Generating OWASP compliance evidence for security reviews and audits
• Defining custom organization rules (no external HTTP calls, no file writes outside /tmp) and enforcing them across all skills

Known Limitations

Regex-based pattern matching cannot detect all injection vectors — some vulnerabilities require semantic analysis of the agent's runtime behavior. The risk scoring is based on static analysis heuristics, not dynamic testing. Skills that legitimately use shell commands (build tools, deployment scripts) may trigger false positives that require ignore-pattern configuration.

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Tags: security prompt-injection owasp ci-cd compliance audit ai-safety

Version: 1.0.0