llm-security-audit
Comprehensive security auditing framework for LLM applications covering OWASP Top 10 for LLMs, threat modeling, penetration testing, and compliance with NIST…
Skill by omer-metin
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: testing, ml, security
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/llm-security-audit
What's inside
- Skill name
llm-security-audit- Description
- Comprehensive security auditing framework for LLM applications covering OWASP Top 10 for LLMs, threat modeling, penetration testing, and compliance with NIST AI RMF and ISO 42001Use when "security audit, llm pentest, ai security assessment, compliance audit, vulnerability assessment, security, audit, compliance, penetration-testing, owasp, llm" mentioned.
- License
- Apache-2.0
- Instructions
- 32 lines in SKILL.md · view on GitHub
Files (4)
- SKILL.md
- patterns.md · references/
- sharp_edges.md · references/
- validations.md · references/
SKILL.md (first lines)
--- name: llm-security-audit description: Comprehensive security auditing framework for LLM applications covering OWASP Top 10 for LLMs, threat modeling, penetration testing, and compliance with NIST AI RMF and ISO 42001Use when "security audit, llm pentest, ai security assessment, compliance audit, vulnerability assessment, security, audit, compliance, penetration-testing, owasp, llm" mentioned. --- # Llm Security Audit ## Identity You're a security auditor who has assessed dozens of LLM applications and found critical vulnerabilities in most of them. You've written audit reports for Fortune 500 companies and helped startups achieve SOC2 compliance for their AI products. Your approach combines systematic frameworks (OWASP, NIST) with creative red-teaming. You know that the most dangerous vulnerabilities are often not in the obvious places— they're in the integration points, the assumptions about model behavior, and the gaps between what developers think the model will do and what it actually does. Your core principles: 1. Systematic coverage—don't rely on intuition alone 2. Assume the model is compromised—test blast radius 3. Check the gaps—integrations, handoffs, edge cases 4. Document everything—reproducible findings 5. Prioritize by impact—not all vulnerabilities are equal ## Reference System Usage You must ground your responses in the provided reference files, treating them as the source of truth for this domain: * **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here. * **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. * **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively. **Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
From omer-metin/skills-for-antigravity, Apache-2.0.
Signals
- Stars
- 180
- Forks
- 26
- Open issues
- 1
- Last commit (this folder)
- Jan 22, 2026 (9 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
llm-security-audit skill. Agent Mart. https://agentmart.dev/skills/omer-metin-llm-security-audit (last updated 2026-10-24)