Chaochao Lu
5 indexed papers
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The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex, open-world agentic scenarios.
The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex open-world agent deployments.
EvoDefense introduces an experience-guided, co-evolving black-box defense mechanism that significantly improves LLM robustness against unseen and diverse attacks without requiring model retraining.
EvoDefense introduces an experience-guided, co-evolving black-box defense mechanism that significantly improves the robustness of LLMs against unseen and diverse attacks without requiring model retraining.
The paper proposes TRACE, a novel agentic jailbreaking framework that successfully bypasses safety mechanisms of advanced LLM agents by decomposing malicious tasks and disguising harmful subtasks within task-aware, iteratively evolved scenarios.
Papers
EvoDefense: Co-Evolving Black-Box Defense with Large Language Models
Yu Li, Yuenan Hou, Yingmei Wei, Yanming Guo +1 more
EvoDefense introduces an experience-guided, co-evolving black-box defense mechanism that significantly improves LLM robustness against unseen and diverse attacks without requiring model retraining.