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 humaisali/Awesome-AI-Skills --skill ai-securitygit clone --depth 1 https://github.com/humaisali/Awesome-AI-SkillsWrote 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/humaisali/awesome-ai-skills/ai-security)<a href="https://agentmods.dev/skills/humaisali/awesome-ai-skills/ai-security"><img src="https://agentmods.dev/badge/skills/humaisali/awesome-ai-skills/ai-security/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/humaisali/awesome-ai-skills/ai-security"><img src="https://agentmods.dev/badge/skills/humaisali/awesome-ai-skills/ai-security.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00048 | $0.03761 |
| Opus 5 | $0.00024 | $0.01880 |
| Sonnet 5 | $0.00010 | $0.00752 |
| Haiku 4.5 | $0.00005 | $0.00376 |
Grade A, and why
ai-security 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 12d 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.
This is a copy
100% identical to ai-security — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Security
AI and LLM security assessment skill for detecting prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse. This is NOT general application security (see security-pen-testing) or behavioral anomaly detection in infrastructure (see threat-detection) — this is about security assessment of AI/ML systems and LLM-based agents specifically.
Table of Contents
- Overview
- AI Threat Scanner Tool
- Prompt Injection Detection
- Jailbreak Assessment
- Model Inversion Risk
- Data Poisoning Risk
- Agent Tool Abuse
- MITRE ATLAS Coverage
- Guardrail Design Patterns
- Workflows
- Anti-Patterns
- Cross-References
Overview
What This Skill Does
This skill provides the methodology and tooling for AI/ML security assessment — scanning for prompt injection signatures, scoring model inversion and data poisoning risk, mapping findings to MITRE ATLAS techniques, and recommending guardrail controls. It supports LLMs, classifiers, and embedding models.
Distinction from Other Security Skills
| Skill | Focus | Approach |
|---|---|---|
| ai-security (this) | AI/ML system security | Specialized — LLM injection, model inversion, ATLAS mapping |
| security-pen-testing | Application vulnerabilities | General — OWASP Top 10, API security, dependency scanning |
| red-team | Adversary simulation | Offensive — kill-chain planning against infrastructure |
| threat-detection | Behavioral anomalies | Proactive — hunting in telemetry, not model inputs |
Prerequisites
Access to test prompts or a prompt test file (JSON array). For gray-box and white-box access levels, written authorization is required before testing. The tool uses static signature matching and does not require live model access — it assesses inputs before they reach the model.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 365 lines · 48 tokens per session scan A dac7c1312e54
ai-security is a skill published in the GitHub repository humaisali/Awesome-AI-Skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 3,761 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-security, differing in 2 lines, and is treated as a copy.
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