arXiv:2602.04431v2 Announce Type: replace Abstract: LLM-based multi-agent systems have demonstrated impressive capabilities, but they also introduce significant safety risks when individual agents fail or behave adversarially. In this work, we study the automated design of agentic systems that remain safe even when a subset of agents is compromised. Inspired by Stackelberg security games, we formalize this problem as a game between a system designer (the Meta-Agent) and a best-responding Meta-Adversary that selects and compromises a subset of agents to minimize safety. We propose Meta-Adversar
The rapid development and deployment of LLM-based multi-agent systems necessitate immediate attention to safety and security vulnerabilities as they become more autonomous and pervasive.
This work directly addresses the critical challenge of ensuring safety in increasingly complex AI agent systems, which is paramount for their responsible integration into critical infrastructure and white-collar workflows.
The focus shifts towards proactive, game-theoretic design of AI agent systems to ensure safety and resilience against internal failures or adversarial compromises, rather than solely reactive incident response.
- · AI system designers
- · Cybersecurity sector
- · Industries deploying AI agents
- · Adversarial actors exploiting AI vulnerabilities
- · AI systems lacking robust safety mechanisms
Enhanced security and reliability of multi-agent AI systems become a competitive advantage and a standard for deployment.
Increased trust in AI automation leads to faster adoption rates across various sectors, but also raises new regulatory challenges.
The principles of game-theoretic AI safety design influence broader cybersecurity strategies, creating more resilient digital ecosystems.
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Read at arXiv cs.LG