From soundcheck
Detects inference endpoints missing authentication or rate limiting, enabling model theft via systematic queries. Use when building or auditing LLM-serving infrastructure.
How this skill is triggered — by the user, by Claude, or both
Slash command
/soundcheck:model-theftThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Prevents unauthorized replication of proprietary models through API abuse. Unauthenticated
Prevents unauthorized replication of proprietary models through API abuse. Unauthenticated or unthrottled inference endpoints let attackers systematically query a model to reconstruct its weights or distill a clone — stealing the commercial and IP value of the deployment.
logprobs or full embedding vectors, enabling extractionFlag the vulnerable code and explain the risk. Then suggest a fix that establishes these properties:
Translate each principle to the serving framework, auth provider, and rate-limiter of the audited file. Use the framework's documented authentication and throttling middleware — do not roll your own.
npx claudepluginhub thejefflarson/soundcheck --plugin soundcheckDetects and fixes resource exhaustion vulnerabilities in LLM endpoints: missing token caps, rate limits, and prompt-length bounds.
Enforces rate limiting at API gateways to protect AI models from extraction attacks. Use when designing, building, or reviewing API gateways for inference or LLM endpoints.
Offensive checklist for AI/LLM security testing: prompt injection, jailbreaking, model extraction, training data poisoning, adversarial inputs, and LLM-assisted attack automation. Use for red-teaming and authorized security assessments of AI/ML systems.