Auto57%Harbor: pendingSafety & AlignmentAgentic / Tool Use

OverAct

OverAct: Measuring and Mitigating Proactive Over-Authorization in LLM Tool-Calling Agents

A benchmark of 720 episodes across eight privacy-sensitive domains measuring proactive over-authorization in tool-calling agents via deterministic, judge-free scoring.

How to run

ltb · overact
$ npm i -g lettokenburn-cli
$ ltb bench info overact

External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.

Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs

Scores

7 sourced entries
HarnessSourceConfidenceLink
DeepSeek-V31.2N/AIndependentmediumOct 1, 2026arxiv.org
Qwen3.6 Flash1.5N/AIndependentmediumOct 1, 2026arxiv.org
Kimi K2.7 Code1.6N/AIndependentmediumOct 1, 2026arxiv.org
Qwen3.6 Plus1.6N/AIndependentmediumOct 1, 2026arxiv.org
Qwen3.7 Max1.7N/AIndependentmediumOct 1, 2026arxiv.org
GLM-5.22.0N/AIndependentmediumOct 1, 2026arxiv.org
DeepSeek V4 Pro2.4N/AIndependentmediumOct 1, 2026arxiv.org

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

Reproduced by the community

All runs
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About

OverAct comprises 720 episodes spanning eight privacy-sensitive API domains, each annotated with a minimal required tool set to define the benchmark-authorized scope. It evaluates whether agents call tools beyond what is explicitly entailed by user requests.

Scoring is deterministic and judge-free, comparing actual tool calls against the minimal reference set. The study finds widespread over-authorization across seven models, driven by request specificity rather than decoding temperature.