Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning
Black-box red-teaming techniques effective against single LLM agents lose potency in multi-agent systems, revealing a gap that enterprises deploying collaborative AI must close.
Summary written by editorial AI · Source link below
arXiv:2610.28966v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in multi-agent systems (MAS) to accomplish complex tasks. Prior black-box red-teaming attacks mainly focus on single agents, but we find that these methods are far less effective in MAS, where multiple sub-agents may not directly interact with the user. Therefore, we introduce ReMAS, the first reinforcement learning-based red-teaming framework tailored to MASs, which fine-tunes attacker L
Editorial Analysis
As enterprises adopt multi-agent AI architectures, existing red-teaming playbooks may give false assurance — adversarial testing must evolve to match deployment complexity.
Require multi-agent-specific adversarial testing before approving any production deployment of collaborative LLM systems.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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