RAID: Refusal-Aware and Integrated Decoding for Jailbreaking LLMs

• URL: http://arxiv.org/abs/2510.13901v1
• Date: Tue, 14 Oct 2025 19:33:09 GMT
• Title: RAID: Refusal-Aware and Integrated Decoding for Jailbreaking LLMs
• Authors: Tuan T. Nguyen, John Le, Thai T. Vu, Willy Susilo, Heath Cooper,
• Abstract summary: RAID (Refusal-Aware and Integrated Decoding) is a framework that crafts adversarial suffixes that induce restricted content while preserving fluency.<n>We show that RAID achieves higher attack success rates with fewer queries and lower computational cost than recent white-box and black-box baselines.
• Score: 17.313975711973374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) achieve impressive performance across diverse tasks yet remain vulnerable to jailbreak attacks that bypass safety mechanisms. We present RAID (Refusal-Aware and Integrated Decoding), a framework that systematically probes these weaknesses by crafting adversarial suffixes that induce restricted content while preserving fluency. RAID relaxes discrete tokens into continuous embeddings and optimizes them with a joint objective that (i) encourages restricted responses, (ii) incorporates a refusal-aware regularizer to steer activations away from refusal directions in embedding space, and (iii) applies a coherence term to maintain semantic plausibility and non-redundancy. After optimization, a critic-guided decoding procedure maps embeddings back to tokens by balancing embedding affinity with language-model likelihood. This integration yields suffixes that are both effective in bypassing defenses and natural in form. Experiments on multiple open-source LLMs show that RAID achieves higher attack success rates with fewer queries and lower computational cost than recent white-box and black-box baselines. These findings highlight the importance of embedding-space regularization for understanding and mitigating LLM jailbreak vulnerabilities.

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