# model-theft

> Claude/Codex skill by [@thejefflarson](https://omgskills.com/library/thejefflarson/index.md) · 20 stars · Updated 2026-07-31

Detects inference endpoints without authentication or throttling, allowing model weight reconstruction. Use when writing inference API endpoints, deploying LLM-serving infrastructure, implementing model access controls, or configuring rate limiting and authentication for model endpoints.

Tags: llms, security, skills

## Install

```sh
git clone https://github.com/thejefflarson/soundcheck /tmp/soundcheck && ln -s /tmp/soundcheck/.claude/skills/model-theft ~/.claude/skills/model-theft
```

## From README

Model Theft (OWASP LLM10:2025) What this checks 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. Vulnerable patterns Inference endpoint has no authentication — any client can query freely Rate limiting applied per IP only, trivially bypassed with rotating proxies Response includes raw logprobs or full embedding vectors, enabling extraction No monitoring for systematic/grid-search query patterns that signal extraction attempts Fix immediately Flag the vulnerable code and explain the risk. Then suggest a fix that establishes these properties: Every inference endpoint requires authentication — API key, bearer token, or mTLS. Unauthenticated endpoints are free training data for anyone who wants to clone the model.

## Links

- HTML page: https://omgskills.com/library/thejefflarson/soundcheck/dot-claude/skills/model-theft/
- GitHub: https://github.com/thejefflarson/soundcheck
- Author: [@thejefflarson](https://omgskills.com/library/thejefflarson/index.md)

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