prompt-injection-defense-auditor
SolidReviews LLM application prompts and input handling for direct and indirect prompt injection vulnerabilities, then writes defensive scaffolding.
Install
Quality Score: 83/100
Skill Content
Details
- Author
- Notysoty
- Repository
- Notysoty/openagentskills
- Created
- 6 months ago
- Last Updated
- 3 weeks ago
- Language
- JavaScript
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
prompt-injection-defense
Contain direct and indirect prompt injection in LLM-integrated applications. Covers source-of-trust tagging, tool-use confirmation after untrusted input, output validation, markdown-image exfiltration prevention, and context-window hygiene. Invoke when building any app where untrusted text reaches an LLM, when the LLM has tools that act on real systems, or after a suspected injection incident.
prompt-injection-audit
Audit an AI system you own or are authorized to test for prompt-injection and tool-poisoning vulnerabilities, then produce a prioritized findings report with concrete mitigations. Covers system prompts, tool/function descriptions, RAG and retrieval pipelines, MCP server integrations, and multi-turn agent flows. Use this skill whenever the user wants to security-review, red-team, threat-model, pen-test, or "check for injection" on an LLM app, agent, chatbot, RAG system, or MCP setup — or asks "is my agent vulnerable to prompt injection?", "audit my system prompt", "review my tool definitions for tool poisoning", or "harden my AI pipeline". Frame every engagement as authorized defensive testing of the user's own system.
prompt-injection-audit
Audit an LLM-backed feature (chatbot, AI assistant, agent, RAG app) for prompt-injection exposure. Read-only analysis that produces findings, not fixes. Use this whenever someone asks whether their AI or LLM feature is safe or secure, whether it can be tricked, jailbroken, or talked into leaking data or ignoring its instructions, or asks for a security or vulnerability review of code that calls a model (OpenAI, Anthropic, LangChain, LlamaIndex, and similar). Triggers on phrasings like "can someone make my chatbot leak other users' data", "is my AI assistant safe from prompt injection", "review my LLM endpoint for security holes", "check if my agent's tools can be abused", or pasting model-calling code and asking if it's exploitable. Do not trigger for general feature-building help with no security question, or for security reviews of code that does not involve an LLM.