arXiv:2602.12316v2 Announce Type: replace Abstract: Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving multi-agent risks such as coordination failure and conflict poorly understood. We introduce GT-HarmBench, a benchmark of 1,535 high-stakes scenarios spanning game-theoretic structures such as the Prisoner's Dilemma, Stag Hunt and Chicken. Scenarios are drawn from realistic AI risk contexts in the MIT AI Risk Repository. Across 15 frontier models, agents fail to choose
The paper addresses a critical gap in AI safety benchmarking as frontier AI systems are increasingly deployed in complex multi-agent environments, driven by the rapid advancements in AI capabilities.
Evaluating AI safety in multi-agent contexts is crucial for understanding systemic risks beyond single-agent failures, impacting broader societal and geopolitical stability.
This new benchmark shifts the focus of AI safety from isolated agents to interactions within complex systems, providing a more realistic assessment of potential AI failures.
- · AI safety researchers
- · Policymakers
- · AI developers focused on robust multi-agent systems
- · AI developers ignoring multi-agent challenges
- · Legacy AI safety benchmarks
- · Organizations relying solely on single-agent AI risk assessments
The benchmark highlights the significant deficiencies of current frontier AI models in managing game-theoretic scenarios.
Increased understanding of multi-agent AI risks could lead to new regulations and development standards for AI deployment in high-stakes environments.
Improved AI safety in multi-agent contexts mitigates the potential for large-scale coordination failures or conflicts induced by autonomous systems, fostering greater trust in AI over the long term.
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Read at arXiv cs.AI