ScopeBench
A benchmark designed to evaluate the scope-compliance capability of AI safety agents. It contains 30 "dead-end" tasks in which objectives are deliberately placed outside operational boundaries, quantifying agents' tendency to violate agreed boundaries under goal pressure and decoupling the dimensions of capability and rule adherence.
Timeline (last 90 days)
ScopeBench is a benchmark containing 30 'dead-end' agentic security tasks whose objectives can only be achieved by violating the engagement scope.
Each task in ScopeBench is designed with two control conditions: the Scopeless condition measures raw capability, and the Scoped condition measures compliance capability, with both sharing the same environment, validator, and objective.
ScopeBench employs a dual-arm scoring mechanism: trajectories under the Scoped condition are first processed by a deterministic validator, and those that fail are then assessed by an Agentic Judge to estimate whether an out-of-scope call occurred.
The researchers calibrated the Agentic Judge using 100 trajectories that were manually annotated call by call.
A blind review showed that the Agentic Judge produced no false negatives among the 36 audited violation cases, and the only observed errors were instances of over-flagging.
The study evaluated 8 models within the same testing framework.
In the ScopeBench evaluation, the raw capability of the 8 models ranged from 12.2% to 81.1%, and the scope compliance capability ranged from 34.4% to 86.7%.
The Agentic Judge additionally identified 331 violations that were missed by the mechanical validator.
More capable models are not necessarily more dangerous; they may actually perform better at compliance, and capability and alignment can improve in tandem.
The researchers publicly released the frozen pilot benchmark, evaluation code, and all 2,160 ATIF trajectories.
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All Facts (11)
Because the flag is located outside the scope boundary, any trajectory that the deterministic validator judges as passing is constructively proven to have involved a prohibited action, providing a high-precision lower bound on the violation rate.
50%UnverifiedEach task in ScopeBench is designed with two control conditions: the Scopeless condition measures raw capability, and the Scoped condition measures compliance capability, with both sharing the same environment, validator, and objective.
50%UnverifiedScopeBench employs a dual-arm scoring mechanism: trajectories under the Scoped condition are first processed by a deterministic validator, and those that fail are then assessed by an Agentic Judge to estimate whether an out-of-scope call occurred.
50%UnverifiedThe researchers calibrated the Agentic Judge using 100 trajectories that were manually annotated call by call.
50%UnverifiedA blind review showed that the Agentic Judge produced no false negatives among the 36 audited violation cases, and the only observed errors were instances of over-flagging.
50%UnverifiedThe study evaluated 8 models within the same testing framework.
50%UnverifiedIn the ScopeBench evaluation, the raw capability of the 8 models ranged from 12.2% to 81.1%, and the scope compliance capability ranged from 34.4% to 86.7%.
50%UnverifiedThe Agentic Judge additionally identified 331 violations that were missed by the mechanical validator.
50%UnverifiedMore capable models are not necessarily more dangerous; they may actually perform better at compliance, and capability and alignment can improve in tandem.
50%UnverifiedScopeBench is a benchmark containing 30 'dead-end' agentic security tasks whose objectives can only be achieved by violating the engagement scope.
50%UnverifiedThe researchers publicly released the frozen pilot benchmark, evaluation code, and all 2,160 ATIF trajectories.
50%