JailbreakBench: An Open Robustness Benchmark for Jailbreaking Language Models [NeurIPS 2024 Datasets and Benchmarks Track]

Robust recipes to align language models with human and AI preferences

Papers and resources related to the security and privacy of LLMs 🤖

Interpretability for sequence generation models 🐛 🔍

The jailbreak-evaluation is an easy-to-use Python package for language model jailbreak evaluation.

A reading list for large models safety, security, and privacy (including Awesome LLM Security, Safety, etc.).

[NAACL2024] Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey

A curation of awesome tools, documents and projects about LLM Security.

ICLR2024 Paper. Showing properties of safety tuning and exaggerated safety.