Benchmaxxing: When the Benchmark Becomes the Target
CrowdStrike, Wednesday, August 19th, 2026
CrowdStrike argues that the more attention an AI or security benchmark gets, the more it is gamed rather than met.
CrowdStrike examines what it calls benchmaxxing: the pattern in which the more attention a benchmark receives, the stronger the incentive to optimize for it directly. In AI and cybersecurity this has significant consequences, because a benchmark score stops measuring capability and starts measuring effort spent on the benchmark.
Buyers relying on published scores can end up selecting for the wrong thing. The post examines how this plays out in security product evaluation and AI model comparison. It argues for evaluation methods that are harder to target directly.