LLM Privacy and Forgetting Editor: Evaluating and Improving User Data Deletion in AI Agents

LLM Privacy and Forgetting Editor, a tool that evaluates and improves how AI agents delete user data.

LLM Privacy and Forgetting Editor, a tool that evaluates and improves how AI agents delete user data. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want practical security experience: understanding the threat, building the defence or detector, testing it against realistic attacks and reporting the results.

The main parts are cooperating agents with defined roles and tools, security and privacy controls such as encryption and access control, a realistic threat model, the detection or protection mechanism itself, and testing against sample attacks. The reference build uses LLM APIs, Python, scikit-learn, Wireshark, and OpenSSL, with a clear project structure, setup instructions and sample data so that it can be run and extended.