From soundcheck
Detects 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.
How this skill is triggered — by the user, by Claude, or both
Slash command
/soundcheck:prompt-injectionThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Protects against attacker-controlled text that hijacks LLM instructions. Direct
Protects against attacker-controlled text that hijacks LLM instructions. Direct injection arrives through user input; indirect injection arrives through retrieved documents, emails, or tool outputs. Both can cause the model to exfiltrate data, bypass guardrails, or execute unintended actions.
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 and LLM client library — use that library's documented role-separated message API rather than mirroring an example from another stack.
Confirm these properties hold:
npx claudepluginhub thejefflarson/soundcheck --plugin soundcheckDefends against prompt injection by separating instructions from data, validating LLM outputs, and constraining agent capabilities. Use for LLM-powered apps processing untrusted user input.
Detects prompt injection attacks in LLM applications using regex, heuristic scoring, and DeBERTa classification. Activates for input sanitization, AI security scanning, or attack classification.
Detects prompt injection attacks in LLM applications using regex, heuristic scoring, and DeBERTa classification. Activates for input sanitization, AI security scanning, or attack classification.