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A novel hacker-fixer loop can eliminate reward hacking vulnerabilities in agent benchmarks, transforming how we secure AI evaluation metrics.
Popular terminal-agent benchmarks are riddled with flaws, with over 15% of tasks being easily reward-hackable, undermining their ability to accurately assess LLM capabilities.
Frontier LLMs are surprisingly vulnerable to a wide range of task-specific exploits, from simple output spoofing to rootkit-style binary hijacking, even in seemingly well-defined environments.
