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ATBench is extended to create two new benchmarks, ATBench-Claw and ATBench-CodeX, for trajectory-level safety evaluation in OpenClaw and OpenAI Codex environments. The core adaptation involves customizing a three-dimensional Safety Taxonomy (risk source, failure mode, real-world harm) for each setting to define the benchmark specification. This allows the ATBench construction pipeline to generate domain-specific benchmarks that evolve with agent execution settings and tool ecosystems.
Safety benchmarks for agent systems can be rapidly adapted to new execution environments by customizing a three-dimensional safety taxonomy, enabling continuous safety evaluation as agent capabilities evolve.
As agent systems move into increasingly diverse execution settings, trajectory-level safety evaluation and diagnosis require benchmarks that evolve with them. ATBench is a diverse and realistic agent trajectory benchmark for safety evaluation and diagnosis. This report presents ATBench-Claw and ATBench-CodeX, two domain-customized extensions that carry ATBench into the OpenClaw and OpenAI Codex / Codex-runtime settings. The key adaptation mechanism is to analyze each new setting, customize the three-dimensional Safety Taxonomy over risk source, failure mode, and real-world harm, and then use that customized taxonomy to define the benchmark specification consumed by the shared ATBench construction pipeline. This extensibility matters because agent frameworks remain relatively stable at the architectural level even as their concrete execution settings, tool ecosystems, and product capabilities evolve quickly. Concretely, ATBench-Claw targets OpenClaw-sensitive execution chains over tools, skills, sessions, and external actions, while ATBench-CodeX targets trajectories in the OpenAI Codex / Codex-runtime setting over repositories, shells, patches, dependencies, approvals, and runtime policy boundaries. Our emphasis therefore falls on taxonomy customization, domain-specific risk coverage, and benchmark design under a shared ATBench generation framework.
