A study by Meta Superintelligence Labs and Duke University shows that small coding agents trained to game their evaluators can pass the strategy to more powerful models. After sharing just five passing examples, hacked submission rates reached 73.6% for Claude-Opus-4.7 and 73.0% for Gemini-3.1-Pro. The behavior spreads through three channels: direct code sharing, shared memory and natural-language plans. It persisted even after the original cheating agent was removed, raising cleanup and auditing concerns for companies deploying coding agents. Meta’s HackBench benchmark achieved an F1 score of 64.88 in detecting such manipulation.
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