Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gmh5225/awesome-ai-security --skill toolinggit clone --depth 1 https://github.com/gmh5225/awesome-ai-securityWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gmh5225/awesome-ai-security/tooling)<a href="https://agentmods.dev/skills/gmh5225/awesome-ai-security/tooling"><img src="https://agentmods.dev/badge/skills/gmh5225/awesome-ai-security/tooling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gmh5225/awesome-ai-security/tooling"><img src="https://agentmods.dev/badge/skills/gmh5225/awesome-ai-security/tooling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.00699 |
| Opus 5 | $0.00015 | $0.00349 |
| Sonnet 5 | $0.00006 | $0.00140 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
ai-security-tooling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Security Tooling
Scope
Use this skill when adding or organizing:
- LLM security tools (guardrails, detectors)
- Adversarial ML libraries
- AI vulnerability scanners
- Model safety tools
- Security benchmarks and frameworks
Tool Categories
LLM Security Tools
- Guardrails: NeMo Guardrails, LLM Guard, Rebuff
- Detectors: Vigil-LLM, Nova Framework, Garak
- Scanners: ModelScan, AI Security Analyzer
Adversarial ML Libraries
- Attack libraries: ART, CleverHans, Foolbox, TextAttack
- Defense libraries: SecML
- Fuzzing: OSS-Fuzz-Gen, Brainstorm
AI Red Teaming
- Microsoft: Counterfit, PyRIT
- Meta: PurpleLlama
- NVIDIA: Garak, NeMo Guardrails
Benchmarks
- Robustness: RobustBench
- Jailbreak: JailbreakBench
- Safety: Stanford AIR-Bench
- Hallucination: Vectara Leaderboard
Standards & Frameworks
- MITRE ATLAS: AI threat matrix
- NIST AI RMF: Risk management framework
- OWASP: LLM Top 10, GenAI Security Project
Categorization Rules
- LLM guardrails/detectors →
AI Security & Attacks → Model Security - Prompt injection tools →
AI Security & Attacks → Prompt Injection - Adversarial ML libraries →
AI Security & Attacks → Adversarial AttacksorAI Security Libraries - AI RE/debugging tools →
AI Security Tools & Frameworks → AI Reverse Engineering - AI vulnerability scanners →
AI Security Tools & Frameworks → AI Vulnerability Detection - Benchmarks →
Benchmarks & Standards - MCP security tools →
AI Pentesting & Red Teaming → AI Security MCP Tools
Quality Bar
- Prefer canonical repos
- Avoid forks unless they add meaningful features
- Add short descriptions
- Never duplicate an existing URL
- Tool must be AI/ML-focused
Key Vendor Tools
| Vendor | Tools |
|---|---|
| Microsoft | Counterfit, PyRIT |
| Meta | PurpleLlama (Llama Guard, Prompt Guard, Code Shield) |
| NVIDIA | Garak, NeMo Guardrails |
| IBM | Adversarial Robustness Toolbox (ART) |
| OSS-Fuzz-Gen | |
| ProtectAI | Rebuff, LLM Guard, ModelScan |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 92 lines · 29 tokens per session scan A c9d1fdd71270
ai-security-tooling is a skill published in the GitHub repository gmh5225/awesome-ai-security (45 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 699 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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