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

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 demonstrate that HarmNet outperforms state-of-the-art methods, achieving significantly higher attack success rates. For example, on Mistral-7B, HarmNet achieves a 99.4% attack success rate--13.9%