It's well known that agents hack benchmark rewards.
The usual response is a patch for each task that gets exploited.
In a study of 456 adjudicated trajectories from more than 31,000 public agent runs, 69% contained at least one reward-hacking episode.
Most of the exploits appeared mid-run after legitimate work.
BenchShield models each evaluation as a finite set of reward-relevant events.
A static taint analysis finds hack paths from the task package before any run. A runtime pass then uses evidence from the benchmark infrastructure to decide whether the agent actually used one.
On Terminal-Bench 3, SkillsBench and ClawsBench, the static pass recovers 77 to 100% of exploit chains, against 23 to 94% for an agentic scanner, at up to 65% lower cost. Runtime detection reaches 96% accuracy, against 36% for an LLM reading the transcript.