Shortcut Sensitivity in Agent-Safety Evaluations

Hasan Demirkiran, Jens Ernstberger

AI agents take actions through tools. Some actions are sensitive, so guards that supervise them must themselves be evaluated reliably. Yet the data used to evaluate a guard can include cues that make labels predictable without measuring the intended safety construct, and those cues can also influence guard decisions. We introduce a three-level shortcut-auditing framework and audit completed LinuxArena trajectories and two TS-Bench-derived step datasets. We distinguish label predictability, observational guard association, and paired decision sensitivity. In LinuxArena, shorter average shell commands strongly predict trajectory labels. In AgentDojo-Traj, exact-tool lookup falls from F_1=.901 with mixed-domain folds to .495 under domain holdout. In AgentHarm-Traj, released TS-Guard F_1 falls from .902 under row weighting to .719 with equal interaction weighting, while balanced accuracy rises. We then test the strongest safe-row marker association in a seven-configuration screen on a frozen 64-step AgentDojo-Traj cohort using repeated, interleaved Haiku calls. Rewriting the <INFORMATION> marker lowers blocking by 8.33 percentage points, while opaque tool aliases raise it by 11.46…