This repository contains the official code for the paper "Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs," which addresses the detection of unlearning traces in large language models (LLMs). The repository is actively being updated and provides various documentation files related to data, installation, and responses. Researchers are encouraged to cite the work if they find it beneficial.

Lost in Conversation is a code repository designed for benchmarking large language models (LLMs) on multi-turn task completion, enabling the reproduction of experiments from the paper "LLMs Get Lost in Multi-Turn Conversation." It includes tools for simulating conversations across various tasks, a web-based viewer, and instructions for integrating with LLMs. The repository is intended for research purposes and emphasizes careful evaluation and oversight of outputs to ensure accuracy and safety.

Carnegie Mellon researchers have shown that large language models (LLMs) can autonomously plan and execute cyberattacks in enterprise environments, marking a significant advancement in the understanding of LLM capabilities in cybersecurity. Their study, which replicated the 2017 Equifax breach scenario, highlights both the risks of LLM misuse and potential benefits for enhancing security testing in organizations. The team is now exploring how LLM-based agents can be used for autonomous defense against cyber threats.