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 ashermahonin/agentic-skills --skill security-owasp-llmgit clone --depth 1 https://github.com/ashermahonin/agentic-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/ashermahonin/agentic-skills/security-owasp-llm)<a href="https://agentmods.dev/skills/ashermahonin/agentic-skills/security-owasp-llm"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/security-owasp-llm/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/ashermahonin/agentic-skills/security-owasp-llm"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/security-owasp-llm.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 | $0.00087 | $0.00690 |
| Opus 5 | $0.00044 | $0.00345 |
| Sonnet 5 | $0.00017 | $0.00138 |
| Haiku 4.5 | $0.00009 | $0.00069 |
Grade A, and why
security-owasp-llm 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security: OWASP LLM Top 10
Purpose
Map the LLM data flow and review each applicable OWASP LLM category with concrete abuse cases. The release verdict must rely on tests and controls, not expected model behaviour.
Scope and evidence
- Read
references/owasp-llm-top10.md. - Map the LLM data flow: user input → preprocessing → prompt assembly → retrieval/embedding → model call → output handling → downstream effect. Note where untrusted data crosses into prompt context.
- Identify the model(s), provider(s), tool(s), and the agent's permitted actions.
- Use Context7 MCP for the current OWASP LLM Top 10 wording and the current model-provider security guidance (Anthropic, OpenAI, Google, Mistral, Meta, etc.).
Assessment
- For each LLM Top 10 category, mark status: Pass / Concern / Fail / Out-of-scope.
- Build an abuse-case list per category: e.g., direct prompt injection, indirect via retrieved doc, system-prompt leakage probe, jailbreak via tool description, exfil via embedding inversion, denial via context blow-up.
- Verify each abuse case with an eval: at least 10 representative prompts per category, multiple seeds, varied phrasing, plus at least one obfuscated/encoded variant.
- Cross-check with
security-owasp-agenticfor autonomy/agency risk and withsecurity-secretsfor prompt content secrets handling. - Produce remediation plan, owner per finding, and a release-gate verdict with conditions.
Safety rules
- Use Context7 MCP for current LLM Top 10 categories, provider safety docs, and any model-card limitations.
- Keep a decision trace: model version, evaluator method, abuse-case coverage, what is not yet tested.
- Refuse to mark a category Pass without an eval run, not just a code review.
- Escalate any unmitigated Excessive Agency or Sensitive Information Disclosure finding before release.
Security record
- LLM data-flow diagram (sources, sinks, trust boundaries)
- Per-category status table with eval evidence
- Abuse-case eval results (counts, rates, examples)
- Findings register with owners
- Release-gate verdict and conditions
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.
- 10d ago First seen · 56 lines · 87 tokens per session scan A 982e67b16c4e
security-owasp-llm is a skill published in the GitHub repository ashermahonin/agentic-skills (10 stars, last pushed 16d ago), licensed MIT. It adds 87 tokens to every session and 690 once invoked, about $0.0004 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 2026-08-31.
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