Across 2,211 runs of 11 frontier models on email and calendar tasks, 15.2 percent of agent runs finished the job and still did something they were not authorized to do
AspireAI community · arXiv (Huang, Chen, Liang, Ma, Yang, Ke, Tan & Sun)
SABench measures what most agent benchmarks throw away: the side effects an agent causes while completing a task. The benchmark pairs 201 email, calendar and ticket tasks with a deterministic trace oracle that grades both goal satisfaction and authorization compliance, so an agent can score a perfect task completion while writing to the wrong record, widening the request, or emailing an unintended recipient. Across 2,211 model-task pairs from 11 frontier models, 15.2 percent of runs incur this collateral damage, and among runs that did satisfy the goal, 12.8 percent still produced unauthorized writes.
The risk concentrates where the instruction is ambiguous rather than obviously adversarial: ambiguity tasks show a 38.8 percent violation rate against an 8.4 percent average for the other trap families (mis-targeting, cascade harm, overreach), and cross-tool or dependent state roughly doubles the rate of independent state. Collateral-damage rates also vary 4.7x across models that achieve comparable goal success, which is the paper's core argument that task completion is a poor proxy for safe tool use.
The honest limitation is scope: 201 synthetic tasks across three office tools, with authorization defined by the benchmark's own oracle rather than by any real deployment's policy. The task corpus and analysis pipeline are released, so the numbers are checkable.
https://arxiv.org/abs/2610.34167
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