6 papers
Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems
Ming Shen, Chao Shang, Sadat Shahriar +4
Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create implicit social expectations: age…
Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
Wei Zou, Mingwen Dong, Miguel Romero Calvo +7
Memory makes LLM-based web agents personalized, powerful, yet exploitable. By storing past interactions to personalize future tasks, agents inadvertently create a persistent attack…
STAC: When Innocent Tools Form Dangerous Chains for LLM Agents
Jing-Jing Li, Jianfeng He, Chao Shang +6
As LLMs advance into autonomous agents with tool-use capabilities, they introduce security challenges that extend beyond traditional content-based LLM safety concerns. This paper i…
The Subtle Art of Defection: Understanding Uncooperative Behaviors in LLM based Multi-Agent Systems
Devang Kulshreshtha, Wanyu Du, Raghav Jain +4
This paper introduces a novel framework for simulating and analyzing how uncooperative behaviors can destabilize or collapse LLM-based multi-agent systems. Our framework includes t…
Cross-Modal Content Optimization for Steering Web Agent Preferences
Tanqiu Jiang, Min Bai, Nikolaos Pappas +2
Vision-language model (VLM)-based web agents increasingly power high-stakes selection tasks like content recommendation or product ranking by combining multimodal perception with p…
Peacemaker or Troublemaker: How Sycophancy Shapes Multi-Agent Debate
Binwei Yao, Chao Shang, Wanyu Du +6
Large language models (LLMs) often display sycophancy, a tendency toward excessive agreeability. This behavior poses significant challenges for multi-agent debating systems (MADS)…