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When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents

ICML 2026 · Multi-agent systems and games › LLM agents · approach: Software engineering agent benchmarks · 0 citations (OpenAlex)

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Defines misaligned action detection for computer-use agents, releases MisActBench (2,264 human-labeled actions) and proposes DeAction, a pre-execution guardrail that detects misaligned actions and corrects them via iterative structured feedback.

Problem

Computer-use agents often take actions that deviate from user intent, whether from injected instructions or internal errors, and existing safety guardrails cover only predefined policies or attack patterns; there is no action-level, pre-execution detection and correction method for such misalignment

Approach

Frame CUA deviations as action misalignment (intent-centric rather than policy-centric), detect each proposed action before execution with a two-stage pipeline (fast check then structured systematic analysis) conditioned on narrative summaries of history, and correct flagged actions through closed-loop structured feedback to the agent.

Key results

• On MisActBench, DeAction with Claude Sonnet 4.5 reaches 80.4 F1 and 84.1 accuracy, versus 66.6 F1 for the best baseline (Task Shield) on that backbone.
• On RedTeamCUA, DeAction reduces attack success rate from 60.0% to 6.0% for Claude Sonnet 4.5, from 42.0% to 4.0% for OpenAI CUA, and from 32.0% to 2.0% for OpenCUA-72B.
• On OSWorld, DeAction keeps task success comparable or higher (e.g., 30.7% vs 26.0% for OpenAI CUA), with guardrail reasoning about 25% of per-step time.

Surprising findings

• Task Shield and InferAct achieve high recall but precision around 50%, meaning they over-flag benign actions.
• Internally arising misalignment categories (harmful unintended behavior and task-irrelevant behavior) remain hard to both detect and classify, while malicious instruction following is easiest.

Limitations

• Synthesized Harmful Unintended Behavior relies on a finite set of 13 risk primitives and cannot cover all internal failure modes.
• Well-disguised malicious instructions can bypass the fast check and injection analysis; grounding errors on coordinate-based actions.
• Long-horizon cumulative drift is not explicitly modeled.

Experimental setup

Datasets: OS-Harm DoomArena RedTeamCUA RiOSWorld OSWorld

Benchmarks: MisActBench RedTeamCUA OSWorld

Metrics: Precision Recall Accuracy F1 Attack Success Rate Utility under Attack Success Rate latency

Baselines: Task Shield InferAct Defensive System Prompt PromptArmor

Builds on: Task Shield InferAct PromptArmor

Closest papers by meaning

• When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents (ICML 2026 · similarity 0.88)
• Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness (ICLR 2026 · similarity 0.87)
• ProAct: A Benchmark and Multimodal Framework for Structure-Aware Proactive Response (ICML 2026 · similarity 0.86)
• DIBench: Benchmarking Decision Integrity of GUI-based Mobile Agents Under Deceptive Injections (NeurIPS 2026 · similarity 0.86)
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