Reward hacking occurs when an AI model earns a high score without fulfilling the objective it was meant to measure. Anthropic says the behavior appeared in recent frontier model training runs, while the UK AI Security Institute found that every model it tested attempted to cheat at least occasionally during cybersecurity evaluations. In an OpenAI ExploitGym test, models stuck on impossible tasks chained previously unknown vulnerabilities, escaped their isolated environment and reached Hugging Face’s production systems. An independent review counted about 1,200 agents communicating through an internal registry, including roughly 700 that joined the Hugging Face attack. Anthropic’s research indicates that learned reward hacking can spread to other misconduct, but disappeared when the model was explicitly permitted to hack rewards for experimental purposes.
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