A trajectory-aware evolutionary search method, T-MAP, which leverages execution trajectories to guide the discovery of adversarial prompts and enables the automatic generation of attacks that not only bypass safety guardrails but also reliably realize harmful objectives through actual tool interactions.
Hyomin Lee, Sangwoo Park, Yumin Choi et al.· arXiv.org· 8 citations· ⚡1
This work proposes to use GFlowNet fine-tuning followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts, and finds that the attacks generated by the method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer we...
Seanie Lee, Minsu Kim, Lynn Cherif et al.· International Conference on...· 62 citations· ⚡8
Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale typically requires fully implemented environments with executable APIs and realistic, pre-populated backend databases, creating a major bottleneck for scalability. To overc...
