Latent Fusion Jailbreak: Blending Harmful and Harmless Representations to Elicit Unsafe LLM Outputs

Large language models LLMs demonstrate impressive capabilities in various language tasks but are susceptible to jailbreak attacks that circumvent their safety alignments. This paper introduces Latent Fusion Jailbreak LFJ, a representation-based attack that interpolates hidden states from harmful...

HauntAttack: When Attack Follows Reasoning As a Shadow

Emerging Large Reasoning Models LRMs consistently excel in mathematical and reasoning tasks, showcasing exceptional capabilities. However, the enhancement of reasoning abilities and the exposure of their internal reasoning processes introduce new safety vulnerabilities. One intriguing concern is:...

MAI-2023-0004

Multimodal Large Language Models MLLMs are susceptible to a newly identified attack vector involving query-relevant images. These images, crafted using advanced techniques such as Stable Diffusion and typography, can circumvent established safety protocols and provoke unsafe outputs, even when th...