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 Strategic-Automation/violin --skill llm-securitygit clone --depth 1 https://github.com/Strategic-Automation/violinWrote 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/strategic-automation/violin/llm-security)<a href="https://agentmods.dev/skills/strategic-automation/violin/llm-security"><img src="https://agentmods.dev/badge/skills/strategic-automation/violin/llm-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/strategic-automation/violin/llm-security"><img src="https://agentmods.dev/badge/skills/strategic-automation/violin/llm-security.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 | $0.00016 | $0.00426 |
| Opus 5.5 | $0.00006 | $0.00170 |
| Sonnet 5.5 | $0.00003 | $0.00085 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
llm-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 1mo 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.
What it actually says
LLM and Agent Security Testing
On-demand skill for AI/LLM application attack surfaces: prompt injection, jailbreaking, and Model Context Protocol (MCP) / JSON-RPC tool and resource exposure. It is distinct from REST/GraphQL API testing; MCP is an agent tool protocol, not a conventional API.
When to Use
- A chatbot, recommendation, generation, or LLM-backed endpoint is in scope.
- A Model Context Protocol endpoint (
initialize,tools/list,tools/call,resources/read) or JSON-RPC surface is discovered. - Do not use for REST/GraphQL/SOAP inventory or authz; use
api-testingfor those, then route the confirmed class back to the right specialist playbook.
Playbook Routing
| Class | Playbook |
|---|---|
| LLM prompt injection, jailbreak, indirect injection | playbooks/llm-prompt-injection.md |
| MCP / JSON-RPC tool, resource, and session testing | playbooks/mcp-api-testing.md |
Common Pitfalls
- Identify the LLM integration and the tool/resource surface before injecting.
- Keep probes read-only and reversible; do not extract or exfiltrate production secrets beyond what proves the flaw.
- Treat a tool that is callable without its expected authorization as a broken-access-control candidate, not merely a protocol finding.
Verification Checklist
- Engagement bootstrap and scope validation succeeded.
- The matched playbook alone was loaded and bound to the PTT task.
- Evidence captures the injected payload, the decisive response, and the intended versus observed behavior.
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.
- 1mo ago First seen · 42 lines · 16 tokens per session scan A 355a9a93646b
llm-security is a skill published in the GitHub repository Strategic-Automation/violin (152 stars, last pushed 2d ago), licensed MIT. It adds 16 tokens to every session and 426 once invoked, about $0.0001 per session on Opus 5.5. 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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