Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment

arXiv:2405.13068v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Current safety alignment techniques, however, harbor inherent vulnerabilities due to their reliance on logit suppression. In this work, we identify critical logit-level vulnerabilities by introducing Semantic-sensitive Alignment and Generation (SSAG), a method designed to systematically manipulate output-layer logits without altering model parameters. Experiments on five popular LLMs show that SSAG exposes harmful responses with a 95% success rate while reducing response time by 86%. VulMine also demonstrates superior attack efficacy, achieving an average ASR of up to 77% against strong defensive mechanisms. These findings reveal crucial weaknesses in existing alignment methods, highlighting an urgent need for improved vulnerability detection and robust safety alignment strategies. Our code is available on github.

Related

• ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs
• ReGA: Model-Based Safeguard for LLMs via Representation-Guided Abstraction
• Latent Instruction Representation Alignment: defending against jailbreaks, backdoors and undesired knowledge in LLMs
• Deliberative Alignment is Deep, but Uncertainty Remains: Inference time safety improvement in reasoning via attribution of unsafe behavior to base model

Source: arXiv cs.LG | 2026-04-21