Genesis: Evolving Attack Strategies for LLM Web Agent Red-Teaming

As large language model LLM agents increasingly automate complex web tasks, they boost productivity while simultaneously introducing new security risks. However, relevant studies on web agent attacks remain limited. Existing red-teaming approaches mainly rely on manually crafted attack strategies...

AutoDAN-Reasoning: Enhancing Strategies Exploration Based Jailbreak Attacks with Test-Time Scaling

Recent advancements in jailbreaking large language models LLMs, such as AutoDAN-Turbo, have demonstrated the power of automated strategy discovery. AutoDAN-Turbo employs a lifelong learning agent to build a rich library of attack strategies from scratch. While highly effective, its test-time...