Functional Homotopy: Smoothing Discrete Optimization via Continuous Parameters for LLM Jailbreak Attacks
<|reference_start|>Functional Homotopy: Smoothing Discrete Optimization via Continuous Parameters for LLM Jailbreak Attacks: Optimization methods are widely employed in deep learning to identify and mitigate undesired model responses. While gradient-based techniques have proven effective for image models, their application to language models is hindered by the discrete nature of the input space. This study introduces a novel optimization approach, termed the \emph{functional homotopy} method, which leverages the functional duality between model training and input generation. By constructing a series of easy-to-hard optimization problems, we iteratively solve these problems using principles derived from established homotopy methods. We apply this approach to jailbreak attack synthesis for large language models (LLMs), achieving a $20\%-30\%$ improvement in success rate over existing methods in circumventing established safe open-source models such as Llama-2 and Llama-3.<|reference_end|>
@article{wang2024functional,
    title={Functional Homotopy: Smoothing Discrete Optimization via Continuous
  Parameters for LLM Jailbreak Attacks},
    author={Zi Wang, Divyam Anshumaan, Ashish Hooda, Yudong Chen, Somesh Jha},
    journal={arXiv preprint arXiv:2410.04234},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04234},
    primaryClass={cs.LG cs.AI cs.CR}
}