16 papers
Twin Agent: Context Residual Compression for Privilege Separated Agents
Zhanhao Hu, Dennis Jacob, Xiao Huang +3
Large language model (LLM) agents are vulnerable to security risks, such as prompt injection attacks from untrusted context that manipulate downstream reasoning and tool use. Exist…
CELEUS: Certifiable and Efficient LLM Evaluation via E-Processes
Zhijian Zhou, Zesheng Ye, Zhaorun Chen +2
Can we trust evaluation scores to capture an LLM's true real-world performance? Certifiable evaluation answers this question by providing guarantee for LLM evaluation. In particula…
FragFuse: Bypassing Access Control of Large Language Model Agents via Memory-Based Query Fragmentation and Fusion
Zixin Rao, Wentian Zhu, Chan Aristella Lu +5
Large language model (LLM) agents increasingly rely on long-term memory to support complex task execution, user personalization, and domain adaptation. Meanwhile, emerging access-c…
MAStrike: Shapley-Guided Collusive Red-Teaming on Multi-Agent Systems
Chejian Xu, Zhaorun Chen, Jingyang Zhang +5
Hierarchical multi-agent systems (MAS) are rapidly being deployed in high-stakes workflows across domains such as finance and software engineering. In these systems, safety and sec…
DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents
Zhaorun Chen, Xun Liu, Haibo Tong +14
AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due to their high capability and fl…
ML-Bench&Guard: Policy-Grounded Multilingual Safety Benchmark and Guardrail for Large Language Models
Yunhan Zhao, Zhaorun Chen, Xingjun Ma +2
As Large Language Models (LLMs) are increasingly deployed in cross-linguistic contexts, ensuring safety in diverse regulatory and cultural environments has become a critical challe…