Perturbed-Embedding-Vectors

Jailbreaking Open-Weight LLMs via Random Embedding Perturbations: runs, labels and code Code and data for the paper Jailbreaking Open-Weight LLMs via Random Embedding Perturbations. Authored by the amazing people of the Theoretical Computer Science Department of University of California, Santa...

TIER: Threat Implicitness Benchmark for Evaluating LLM Safety Behaviors

Current LLM safety benchmarks largely rely on binary metrics, overlooking how models respond to harmful prompts with varying threat implicitness. We introduce TIER, a Threat Implicitness Benchmark for behavioral safety evaluation of LLMs. TIER covers four risk domains and four threat levels, from...

Sockpuppetting: Jailbreaking LLMs without Optimization through Output Prefix Injection

As open-weight large language models LLMs increase in capabilities, safeguarding them against malicious prompts and understanding possible attack vectors becomes ever more important. While automated jailbreaking methods like GCG Zou et al., 2023 remain effective, they often require substantial...