How Brittle is Agent Safety? Rethinking Agent Risk under Intent Concealment and Task Complexity
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
Current safety evaluations for LLM-driven agents primarily focus on atomic harms, failing to address sophisticated threats where malicious intent is concealed or diluted within complex tasks. We address this gap with a two-dimensional analysis of agent safety brittleness under the orthogonal pressures of intent concealment and task complexity. To enable this, we introduce OASIS (Orthogonal Agent Safety Inquiry Suite), a hierarchical benchmark with fine-grained annotations and a high-fidelity simulation sandbox. Our findings reveal two critical phenomena: safety alignment degrades sharply and predictably as intent becomes obscured, and a "Complexity Paradox" emerges, where agents seem safer on harder tasks only due to capability limitations. By releasing OASIS and its simulation environment, we provide a principled foundation for probing and strengthening agent safety in these overlooked dimensions.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 12 of its 35 references that are in the map. First posted on arXiv 11 Nov 2025.
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12 of 35 references.
References · 12
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
- 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
- 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
- 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 272
- 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Taskscited by 190
- 2025Evaluation and Benchmarking of LLM Agents: A Surveycited by 145
- 2024StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Modelscited by 129
- 2025MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Serverscited by 101
- 2025MCPVerse: An Expansive, Real-World Benchmark for Agentic Tool Usecited by 16
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- 2026The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents
- 2026Toward a Principled Framework for Agent Safety Measurement
- 2025OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety
- 2026Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents
- 2026Risky-Bench: Probing Agentic Safety Risks under Real-World Deployment
- 2025A Safety and Security Framework for Real-World Agentic Systems
- 2026Hiding in Plain Sight: Decoupling Pretext from Actuation for Skill Poisoning in LLM Agents
- 2026OpenSkillRisk: Benchmarking Agent Safety When Using Real-World Risky Third-Party Skills