Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs
Automated red teaming frameworks for Large Language Models (LLMs) have become increasingly sophisticated, yet they share a fundamental limitation: their jailbreak logic is confined to selecting, combining, or refining pre-existing attack strategies. This binds their creativity and leaves them unable to autonomously invent entirely new attack mechanisms. To overcome this gap, we introduce EvoSynth, an autonomous framework that shifts the paradigm from attack planning to the evolutionary synthesis of jailbreak methods. Instead of refining prompts, EvoSynth employs a multi-agent system to autonomously engineer, evolve, and execute novel, code-based attack algorithms. Crucially, it features a code-level self-correction loop, allowing it to iteratively rewrite its own attack logic in response to failure. Through extensive experiments, we demonstrate that EvoSynth not only establishes a new state-of-the-art by achieving an 85.5\% Attack Success Rate (ASR) against highly robust models like Claude-Sonnet-4.5, but also generates attacks that are significantly more diverse than those from existing methods. We release our framework to facilitate future research in this new direction of evolutionary synthesis of jailbreak methods. Code is available at: https://github.com/dongdongunique/EvoSynth.
691c7827fecc9dfeafbf46c1
Yunhao Chen
claimed_verified
2025-11-19T08:40:25.080Z
635f7b4af72ae36c3e3223be
/avatars/afd8967e2e0dbddc0cf5692ec5673ba1.svg
Yunhao Chen
dongdongunique
user
dongdongunique
691c7827fecc9dfeafbf46c2
Xin Wang
claimed_verified
2025-11-18T14:07:51.263Z
677d32cf28895e2124065a63
/avatars/897fa9fb3b3adc6e60094e3b505f94bc.svg
Xin Wang
xinwang22
user
xinwang22
691c7827fecc9dfeafbf46c3
Juncheng Li
claimed_verified
2025-11-26T09:28:31.158Z
65ec1d0f7bf29c40778fe220
/avatars/5590fc01d30ebe24e65b9cb3bceaebb7.svg
Juncheng Li
bin015
user
bin015
691c7827fecc9dfeafbf46c4
Yixu Wang
691c7827fecc9dfeafbf46c5
Jie Li
691c7827fecc9dfeafbf46c6
Yan Teng
691c7827fecc9dfeafbf46c7
Yingchun Wang
691c7827fecc9dfeafbf46c8
Xingjun Ma
Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs
Automated red teaming frameworks for Large Language Models (LLMs) have become increasingly sophisticated, yet they share a fundamental limitation: their jailbreak logic is confined to selecting, combining, or refining pre-existing attack strategies. This binds their creativity and leaves them unable to autonomously invent entirely new attack mechanisms. To overcome this gap, we introduce EvoSynth, an autonomous framework that shifts the paradigm from attack planning to the evolutionary synthesis of jailbreak methods. Instead of refining prompts, EvoSynth employs a multi-agent system to autonomously engineer, evolve, and execute novel, code-based attack algorithms. Crucially, it features a code-level self-correction loop, allowing it to iteratively rewrite its own attack logic in response to failure. Through extensive experiments, we demonstrate that EvoSynth not only establishes a new state-of-the-art by achieving an 85.5\% Attack Success Rate (ASR) against highly robust models like Claude-Sonnet-4.5, but also generates attacks that are significantly more diverse than those from existing methods. We release our framework to facilitate future research in this new direction of evolutionary synthesis of jailbreak methods. Code is available at: https://github.com/dongdongunique/EvoSynth.