Do Internal Layers of LLMs Reveal Patterns for Jailbreak Detection?
This addresses the security issue of adversarial prompt attacks on LLMs for users and developers, but it is incremental as it presents preliminary findings without concrete performance gains.
The study tackled the problem of jailbreaking in large language models by analyzing internal layer representations of GPT-J and Mamba2, finding distinct behaviors between jailbreak and benign prompts that suggest potential for improved detection.
Jailbreaking large language models (LLMs) has emerged as a pressing concern with the increasing prevalence and accessibility of conversational LLMs. Adversarial users often exploit these models through carefully engineered prompts to elicit restricted or sensitive outputs, a strategy widely referred to as jailbreaking. While numerous defense mechanisms have been proposed, attackers continuously develop novel prompting techniques, and no existing model can be considered fully resistant. In this study, we investigate the jailbreak phenomenon by examining the internal representations of LLMs, with a focus on how hidden layers respond to jailbreak versus benign prompts. Specifically, we analyze the open-source LLM GPT-J and the state-space model Mamba2, presenting preliminary findings that highlight distinct layer-wise behaviors. Our results suggest promising directions for further research on leveraging internal model dynamics for robust jailbreak detection and defense.