Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning↗ (원문, 새 창)

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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