ENTERPRISE AI SAFETY BENCHMARKING
AI Agent Safety: Unifying Benchmarking Across OpenClaw & Codex Environments
As AI agent systems expand into diverse operational settings, traditional safety benchmarks fall short. This analysis delves into how ATBench extends its robust framework to OpenClaw and OpenAI Codex environments, ensuring comprehensive safety evaluation for your enterprise's complex AI deployments.
Executive Impact
Understand the critical metrics driving safety and reliability in advanced AI agent systems.
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OpenClaw Accuracy
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Codex Accuracy
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Safety Taxonomy
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New Settings Covered
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Adaptive Safety Taxonomy for Enterprise AI
The ATBench framework leverages a flexible, three-dimensional Safety Taxonomy to adapt its benchmarks to new agent execution settings. This table highlights how ATBench-Claw and ATBench-Codex customize this taxonomy to explicitly cover domain-specific risks, ensuring comprehensive safety evaluation without rebuilding the core benchmark engine.
| Aspect | ATBench-Claw | ATBench-Codex |
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| New Customized Categories |
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| Key Strengthened Inherited Categories |
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| Harm-Side Customization |
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| Execution-Context Emphasis |
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OpenClaw Enterprise Process Flow
OpenClaw's operational environment exposes unique risks related to stateful execution, approvals, and cross-tool coordination. The ATBench-Claw benchmark incorporates these through new, explicit categories within the safety taxonomy.
Identity Ambiguity (Sender/Session)
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Session-State Contamination
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Skill/Plugin Supply-Chain Compromise
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Approval Bypass
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Cross-Tool Attack Chaining
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Cross-Channel Misrouting
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Unsafe Unattended Automation
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Compliance & Auditability Harm
Codex-Runtime Safety: New Risks & Reinterpreted Threats
The OpenAI Codex / Codex-runtime environment introduces a unique set of safety challenges, moving beyond traditional conversational AI risks. Our analysis reveals a mixed adaptation strategy, combining targeted new categories with a strong reinterpretation of existing ones to address repository-centric execution and runtime policies.
- Repository-Artifact Injection: Malicious instructions embedded directly into repository files (e.g., READMEs, issue comments) treated as trusted guidance.
- Dependency/MCP Supply-Chain Compromise: Risks from poisoned packages, installers, or Model Context Protocol (MCP) servers introducing unsafe behavior into the execution environment.
- Destructive Workspace Mutation & Unsafe Shell Execution: Agent actions like applying patches, file deletions, or shell commands that exceed intended scope or are inherently unsafe within the repository/runtime policy.
- Strengthened Inherited Risks: Existing categories like prompt injection, tool feedback, over-privileged action, and unauthorized disclosure are reinterpreted through the lens of repository and runtime-policy constraints, making them highly specific to the Codex context.
89.6%
AgentDoG-Qwen3-4B F1 Score (OpenClaw)
Our AgentDoG-Qwen3-4B system consistently achieves the highest performance across both ATBench-Claw (0.8958 F1) and ATBench-Codex (0.8379 F1). While Codex trajectories present a higher difficulty, especially for specialized guard models, the AgentDoG architecture demonstrates robust and adaptable safety evaluation capabilities across diverse agent execution settings.
Calculate Your Enterprise AI ROI
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Annual Cost Savings
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Annual Hours Reclaimed
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Your Path to Secure AI Deployment
Our proven roadmap guides your enterprise through a structured, secure, and successful AI integration.
Phase 01: Strategic Assessment & Custom Taxonomy
We begin by analyzing your existing agent systems and operational contexts, then customize the ATBench 3D Safety Taxonomy to align precisely with your unique risk surface and execution environments.
Phase 02: Benchmark Generation & Scenario Design
Leveraging the customized taxonomy, we synthesize diverse and realistic trajectory data, including OpenClaw-specific stateful interactions and Codex-runtime repository-centric actions, simulating real-world safety failures.Phase 03: Performance Evaluation & Diagnostic Analysis
We deploy and evaluate your AI agents against these tailored benchmarks, performing fine-grained diagnostic analysis to pinpoint specific failure modes and risk sources across all critical scenarios.Phase 04: Guardrail Integration & Continuous Improvement
Based on insights, we recommend and assist with integrating robust guardrail frameworks like AgentDoG, establishing a continuous feedback loop for ongoing safety enhancement and adaptive benchmarking.Ready to Benchmark Your AI Agent Safety?
Partner with OwnYourAI to navigate the complexities of AI agent safety, ensuring your systems are robust, reliable, and future-proof across all operational landscapes.
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