Published 7/29/2026
Analyzed 8/13/2026
AgentS4D measures unsafe actions and state changes across complete workspace-agent executions rather than treating task completion or isolated model responses as safety evidence. Its 328 sandboxed cases introduce risky content through user requests, documents, web resources, tools, third-party skills, and persistent memory, then compare the same cases across four agent harnesses and five model backends.
AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents
Evaluated models: GPT-5.5, Gemini 3.1 Pro, DeepSeek V4-Pro +2 more
Source: arXiv