HarmNet: A Framework for Adaptive Multi-Turn Jailbreak Attacks on Large Language Models

Authors: Sidhant Narula, Javad Rafiei Asl, Mohammad Ghasemigol, Eduardo Blanco, Daniel Takabi
Published: 2025-10-21

Source: https://arxiv.org/abs/2510.18728

PDF: https://arxiv.org/pdf/2510.18728

AIにより推定されたラベル

脱獄手法 大規模言語モデル クエリ生成手法

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Abstract

Large Language Models (LLMs) remain vulnerable to multi-turn jailbreak attacks. We introduce HarmNet, a modular framework comprising ThoughtNet, a hierarchical semantic network; a feedback-driven Simulator for iterative query refinement; and a Network Traverser for real-time adaptive attack execution. HarmNet systematically explores and refines the adversarial space to uncover stealthy, high-success attack paths. Experiments across closed-source and open-source LLMs show that HarmNet outperforms state-of-the-art methods, achieving higher attack success rates. For example, on Mistral-7B, HarmNet achieves a 99.4 Index terms: jailbreak attacks; large language models; adversarial framework; query refinement.