AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents

AgentHazard is a research benchmark project (arXiv paper, 3 days old, 0 stars/forks) with no deployed artifact or community adoption. While the problem domain—evaluating harmful behavior in computer-use agents—is timely and frontier-relevant, the project itself is in nascent prototype stage. The core contribution is a novel framing of multi-step behavioral chains in agent safety (combining known evaluation techniques with agent-specific threat modeling), not a production tool or reusable component. Defensibility is low because: (1) it's pre-release, (2) no lock-in or switching costs, (3) trivially reproducible as a benchmark dataset/evaluation script once published. Frontier risk is HIGH because: (1) OpenAI, Anthropic, Google, and DeepSeek are actively shipping computer-use agents (GPT-4o with vision, Claude Computer Use, Gemini 2.0, etc.), (2) safety evaluation frameworks are strategic assets for LLM labs deploying agent products, (3) a frontier lab could trivially incorporate this benchmark into their own safety pipelines or publish a competing benchmark, (4) the paper itself will likely be cited/integrated by frontier safety teams rather than the project becoming an independent tool. The work has genuine novelty in problem formulation (identifying harmful-via-sequence risks) but is deliverable as a paper, dataset, and reference implementation—not as defensible IP. Integration surface is reference_implementation because the benchmark's value comes from the task design and dataset, not from shipping code.