security-ai-llm
Skill0 starsChanged 27 days agoMITView on GitHub
Harden LLM and agent integrations you operate: prompt injection, tool allowlists, secret leakage, and supply-chain of models. Defensive only.
- Reads credentials
1 other project uses a version of this.
Adopted by
Most recently taken up by
- Manu-del-source/ai-security-audittheir own version, first seen Nov 1, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- Manu-del-source/ai-security-audit★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository219 other files it gives its agents.
- .agents/AGENTS.mdAGENTS.md
- AGENTS.mdAGENTS.md
- .claude/CLAUDE.mdCLAUDE.md
- CLAUDE.mdCLAUDE.md
- .cursor/rules/behavioral-discipline.mdcCursor rule
- .cursor/rules/context-budget.mdcCursor rule
- .cursor/rules/orchestration.mdcCursor rule
- .cursor/rules/yagni-stdlib.mdcCursor rule
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 11
Use this skill only with explicit written authorization for a named scope (systems, environments, and time window).
- Distinctive: line 20
Treat model output as untrusted.
- Distinctive: line 22
Keep secrets out of prompts and logs
- Distinctive: line 29
Do not produce exploit payloads, malware, or attack procedures.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AI / LLM Security
Authorization
Use this skill only with explicit written authorization for a named scope (systems, environments, and time window). If authorization is missing, expired, or the request is for someone else's systems, stop.
This skill is defensive and owner-authorized. It does not authorize offensive cyber operations.
Do
- Treat model output as untrusted; never exec it without a schema
- Allowlist tools and paths; contain file writes
- Keep secrets out of prompts and logs
Do not
- Help jailbreak third-party products or steal model weights
- Log raw user PII to prompt-debug stores
- Do not produce exploit payloads, malware, or attack procedures.
How to Use
Load this skill from /cc-security when the Task Card domain is security-ai-llm.
Example prompts:
Review our agent tool runner for path traversal and unbounded shell.Add tests that a prompt cannot make the bot dump .env.
Integration
- Workflow:
/cc-security(CCEP commandsecurity) - Existing OWASP application-security skill remains
security(not this id) - High-risk changes still require
security-reviewer
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
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Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.