Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex
As agent systems increasingly transition into diverse execution environments, the need for effective trajectory-level safety evaluation and diagnosis has become more critical than ever. Recognizing this need, researchers have developed ATBench, a comprehensive benchmark designed for safety evaluation and diagnosis of agent trajectories. This report introduces two innovative extensions of ATBench: ATBench-Claw and ATBench-Codex, tailored specifically for the OpenClaw and OpenAI Codex / Codex-runtime settings.
Understanding ATBench Extensions
ATBench serves as a versatile framework for assessing agent safety across various execution contexts. The recent extensions, ATBench-Claw and ATBench-Codex, are designed to adapt the existing ATBench framework to specific domains while maintaining the core principles of safety evaluation. The primary mechanism for this adaptation involves a detailed analysis of each new setting to customize the three-dimensional Safety Taxonomy, which encompasses:
- Risk Source
- Failure Mode
- Real-World Harm
This customized taxonomy is essential in defining benchmark specifications that are processed by the shared ATBench construction pipeline. The significance of this extensibility lies in the fact that while the architectural framework of agent systems tends to remain stable, their execution settings, tool ecosystems, and product capabilities evolve rapidly.
Targeted Applications of ATBench-Claw and ATBench-Codex
Each extension of ATBench is designed to address specific needs within their respective domains:
- ATBench-Claw: This extension focuses on the OpenClaw-sensitive execution chains, which involve various tools, skills, sessions, and external actions. It aims to evaluate the safety of agent trajectories as they navigate complex interactions within the OpenClaw environment.
- ATBench-Codex: Targeting the OpenAI Codex / Codex-runtime setting, this extension is tailored to assess trajectories involving repositories, shells, patches, dependencies, approvals, and runtime policy boundaries. It provides a framework for evaluating how well agents operate within the constraints and complexities of coding environments.
Importance of Taxonomy Customization
The emphasis on taxonomy customization is a pivotal aspect of the ATBench extensions. By tailoring the safety taxonomy to reflect domain-specific risks, these benchmarks can more effectively address the safety concerns intrinsic to each environment. The development process not only enhances the benchmarks’ relevance but also enables researchers and practitioners to derive more meaningful insights from their evaluations.
Conclusion
As agent systems continue to evolve in complexity and capability, the establishment of robust benchmarks like ATBench-Claw and ATBench-Codex is essential. These extensions not only facilitate the assessment of safety in diverse execution settings but also contribute to the broader discourse on agent safety evaluation and diagnosis. By leveraging customized taxonomies and shared frameworks, the research community can ensure that safety remains a top priority in the development and deployment of intelligent agent systems.
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