Adaptive Privacy-Aware Context Selection for Local LLM-Based Vulnerability Analysis

An adaptive, privacy-aware context selection method for vulnerability analysis with local LLMs.

An adaptive, privacy-aware context selection method for vulnerability analysis with local LLMs. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend 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 security and privacy controls such as encryption and access control, a realistic threat model, the detection or protection mechanism itself, testing against sample attacks, and reporting and alerting of findings. The reference build uses LLM APIs, NLTK, Python, scikit-learn, and Wireshark, with a clear project structure, setup instructions and sample data so that it can be run and extended.