[2604.04060] CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks

[2604.04060] CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks

arXiv - AI 3 min read

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Abstract page for arXiv paper 2604.04060: CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks

Computer Science > Cryptography and Security arXiv:2604.04060 (cs) [Submitted on 5 Apr 2026] Title:CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks Authors:Siyuan Li, Zehao Liu, Xi Lin, Qinghua Mao, Yuliang Chen, Haoyu Li, Jun Wu, Jianhua Li, Xiu Su View a PDF of the paper titled CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks, by Siyuan Li and 8 other authors View PDF HTML (experimental) Abstract:As Large Language Models (LLMs) are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety concerns, especially those evolving over multi-round interactions. Existing defenses are largely reactive and struggle to adapt as adversaries refine strategies across rounds. In this work, we propose CoopGuard , a stateful multi-round LLM defense framework based on cooperative agents that maintains and updates an internal defense state to counter evolving attacks. It employs three specialized agents (Deferring Agent, Tempting Agent, and Forensic Agent) for complementary round-level strategies, coordinated by System Agent, which conditions decisions on the evolving defense state (interaction history) and orchestrates agents over time. To evaluate evolving threats, we introduce the EMRA benchmark with 5,200 adversarial samples across 8 attack types, simulating progressively LLM multi-round attacks. Experiments show that CoopGuard reduces attack su...

Originally published on April 07, 2026. Curated by AI News.

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