From soundcheck
Detects RAG pipelines that ingest external documents into LLM context without sanitization or trust gating. Flag vulnerable patterns like direct concatenation, unbounded retrieval, and SSRF-through-fetch.
How this skill is triggered — by the user, by Claude, or both
Slash command
/soundcheck:rag-securityThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Prevents prompt injection through retrieved documents and uncontrolled content flooding
Prevents prompt injection through retrieved documents and uncontrolled content flooding into LLM context. Attacker-controlled documents can override system instructions, exfiltrate data, or manipulate model behavior when injected without guardrails.
Flag the vulnerable code and explain the risk. Then suggest a fix that establishes these properties. Translate each property into the audited file's language, HTTP client, and LLM API — use the documented secure primitives of that stack.
ssrf skill for outbound HTTP.prompt-injection
skill for the trust-tier pattern.Confirm the response:
npx claudepluginhub thejefflarson/soundcheck --plugin soundcheckDetects direct and indirect prompt injection in LLM applications. Flags user input or retrieved documents that could hijack model instructions, and enforces trust-tier separation, input screening, and output validation.
Audit applications for AI prompt injection, agent security, and LLM permission boundary vulnerabilities. Use when securing AI features or agents.
Defends against prompt injection by separating instructions from data, validating LLM outputs, and constraining agent capabilities. Use for LLM-powered apps processing untrusted user input.