SafeInt: Shielding Large Language Models from Jailbreak Attacks via Safety-Aware Representation Intervention
This addresses safety vulnerabilities in LLMs for real-world deployment, offering a more effective and efficient defense against jailbreak attacks, though it is an incremental improvement over existing representation-based methods.
The paper tackles the problem of jailbreak attacks on large language models (LLMs) by proposing SafeInt, a defense method that intervenes in representations to relocate jailbreak-related ones into a rejection region, achieving superior performance over baselines in experiments across six attacks and two datasets while largely maintaining utility.
With the widespread real-world deployment of large language models (LLMs), ensuring their behavior complies with safety standards has become crucial. Jailbreak attacks exploit vulnerabilities in LLMs to induce undesirable behavior, posing a significant threat to LLM safety. Previous defenses often fail to achieve both effectiveness and efficiency simultaneously. Defenses from a representation perspective offer new insights, but existing interventions cannot dynamically adjust representations based on the harmfulness of the queries. To address this limitation, we propose SafeIntervention (SafeInt), a novel defense method that shields LLMs from jailbreak attacks through safety-aware representation intervention. Built on our analysis of the representations of jailbreak samples, the core idea of SafeInt is to relocate jailbreak-related representations into the rejection region. This is achieved by intervening in the representation distributions of jailbreak samples to align them with those of unsafe samples. We conduct comprehensive experiments covering six jailbreak attacks, two jailbreak datasets, and two utility benchmarks. Experimental results demonstrate that SafeInt outperforms all baselines in defending LLMs against jailbreak attacks while largely maintaining utility. Additionally, we evaluate SafeInt against adaptive attacks and verify its effectiveness in mitigating real-time attacks.