11 papers
AutoDojo: Adaptive Black-Box Attacks Reveal the Limits of IPI Defenses and Task-Specification Effects in LLM Agents
Xinhang Ma, Taoran Li, Chaowei Xiao +3
Indirect prompt injection (IPI) is a major security threat to LLM-powered agents. Thus, a growing body of work have proposed a variety of defensive approaches against IPI. These ca…
Think Twice Before You Act: Protecting LLM Agents Against Tool Description Poisoning via Isolated Planning
Shanghao Shi, Xiao Wang, Chaoyu Zhang +6
The integration of external tools has substantially expanded the capabilities of large language model (LLM) agents, but it also introduces new attack surfaces beyond prompt injecti…
From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning
Shanghao Shi, Chaoyu Zhang, Heng Jin +6
Federated learning (FL) enables multiple parties to collaboratively fine-tune language models for domain-specific tasks without sharing raw data. Since full model fine-tuning is of…
Planning to Hammer: Difficulty-Aware Decomposition for Automating Rocq Proofs
Ning Zhang, Nongyu Di, Zenan Li +2
As AI-generated code proliferates, formal verification, particularly through interactive theorem provers such as Rocq and Isabelle, becomes increasingly important for ensuring soft…
TokenPilot: Cache-Efficient Context Management for LLM Agents
Buqiang Xu, Zirui Xue, Dianmou Chen +12
As LLM agents are deployed in long-horizon sessions, context accumulation drives up inference costs. Existing approaches utilize text pruning or dynamic memory eviction to minimize…
AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?
Hao Li, Ruoyao Wen, Shanghao Shi +3
AI agents that autonomously interact with external tools and environments have shown great promise across real-world applications. However, their reliance on external data exposes…