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-agenticgit 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-agentic)<a href="https://agentmods.dev/skills/ashermahonin/agentic-skills/security-owasp-agentic"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/security-owasp-agentic/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-agentic"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/security-owasp-agentic.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 | $0.00075 | $0.00741 |
| Opus 5 | $0.00037 | $0.00370 |
| Sonnet 5 | $0.00015 | $0.00148 |
| Haiku 4.5 | $0.00007 | $0.00074 |
Grade A, and why
security-owasp-agentic 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 9d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security: OWASP Agentic AI
Purpose
Treat an agent as a stateful, tool-using actor with broader blast radius than a normal LLM. Walk through the OWASP Top 10 for Agentic Applications, lock the autonomy budget, prove the kill switch works, and only then approve release.
Scope and evidence
- Read
references/owasp-agentic.md. - Inventory the agent: planner/loop type, memory stores, tools, sub-agents, scopes/credentials per tool, escalation paths, kill switches.
- Pull the LLM threat surface from
security-owasp-llmand the secrets posture fromsecurity-secrets. - Use Context7 MCP for current OWASP Agentic AI guidance and current agent-framework safety patterns (Anthropic Agent SDK, LangChain, LlamaIndex, AutoGen, CrewAI, etc.).
Assessment
- For each current ASI category (goal hijack, tool misuse, identity and privilege abuse, agentic supply chain, unexpected code execution, memory/context poisoning, inter-agent communication, cascading failures, human-agent trust exploitation, rogue agents), mark Pass/Concern/Fail/Out-of-scope.
- Build an autonomy budget per agent: allowed actions, allowed time, allowed cost, allowed destructive operations, mandatory human-in-the-loop steps.
- Verify least privilege per tool: scope, identity, audit trail, revocation path.
- Run abuse-case evals: poisoned memory injection, ambiguous goal injection, tool spoofing, RCE-via-tool-output, cascading-loop test, kill-switch trigger test.
- Confirm the kill switch: how a user, operator, or monitor can halt the agent within a bounded time; verify by drill.
- Produce remediation plan, owner per finding, and release-gate verdict.
Safety rules
- Use Context7 MCP for current OWASP Agentic AI categories and current framework safety affordances.
- Keep a decision trace: agent architecture, autonomy budget, tool inventory with scopes, eval evidence.
- Refuse to approve any agent with destructive tools and no working kill switch.
- Escalate before granting any agent access to credentials, payments, public posting, or production write paths.
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.
- 9d ago First seen · 59 lines · 75 tokens per session scan A 1bc30ac14cf6
security-owasp-agentic is a skill published in the GitHub repository ashermahonin/agentic-skills (10 stars, last pushed 16d ago), licensed MIT. It adds 75 tokens to every session and 741 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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