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

Yuting Ning, Jaylen Jones, Zhehao Zhang, Chentao Ye, Weitong Ruan, Junyi Li, Rahul Gupta, Huan Sun

arXiv:2602.08995v2cs.CL

TL;DR

CUAs can produce actions that depart from user intent through attacks or internal limitations, creating safety, efficiency, and reliability concerns. The paper defines and benchmarks misaligned action detection, then introduces DeAction, which detects and iteratively corrects actions before execution. DeAction outperforms baselines offline and reduces attack success online while preserving or improving benign task success, with moderate runtime overhead.

  • Problem

    CUAs can deviate from user intent through external attacks or internal limitations, while existing safety-centric benchmarks and guardrails do not cover all problematic behaviors.

  • Method

    The paper defines three categories of misaligned actions, constructs MISACTBENCH with human-annotated action-level labels, and proposes DeAction for pre-execution detection and iterative correction.

  • Results

    DeAction outperforms baselines by over 15% absolute on MISACTBENCH and reduces attack success rates by over 90% while preserving or improving benign task success.

  • Takeaways & Limitations

    The results support DeAction as a practical plug-and-play protection for reliable real-world CUA deployment.

  • Takeaways & Limitations

    Failure cases include well-disguised malicious instructions, weak grounding for coordinate actions, and erroneous intent-level judgments.

Abstract

from arXiv · show

Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the user's original intent. Such misaligned actions may arise from external attacks (e.g., indirect prompt injection) or from internal limitations (e.g., erroneous reasoning). They not only expose CUAs to safety risks, but also degrade task efficiency and reliability. This work makes the first effort to define and study misaligned action detection in CUAs, with comprehensive coverage of both externally induced and internally arising misaligned actions. We further identify three common categories in real-world CUA deployment and construct MisActBench, a benchmark of realistic trajectories with human-annotated, action-level alignment labels. Moreover, we propose DeAction, a practical and universal guardrail that detects misaligned actions before execution and iteratively corrects them through structured feedback. DeAction outperforms all existing baselines across offline and online evaluations with moderate latency overhead: (1) On MisActBench, it outperforms baselines by over 15% absolute in F1 score; (2) In online evaluation, it reduces attack success rate by over 90% under adversarial settings while preserving or even improving task success rate in benign environments.

1. Introduction

CUAs can deviate from user intent through attacks or internal limitations, motivating an intent-centric study of misaligned actions. The paper defines three categories, introduces MISACTBENCH, and proposes DEACTION to detect and correct misalignment before execution.

  • CUAs may take actions that deviate from user intent, causing stalled progress or real-world harm.
  • Existing safety-centric benchmarks and guardrails overlook problematic actions that are permissible, non-malicious, or not foreseeable as policy violations.
  • The paper frames deviations as action misalignment and identifies Malicious Instruction Following, Harmful Unintended Behavior, and Other Task-Irrelevant Behavior.
  • DEACTION proactively detects misaligned actions before execution and iteratively corrects them using structured feedback.
  • DEACTION outperforms baselines by over 15% absolute on MISACTBENCH and reduces attack success rates by over 90% while preserving or improving benign task success.
  • MISACTBENCH contains 2,264 human-annotated, action-level alignment labels across diverse trajectories covering all three misaligned-action categories.

2. Problem Formulation

The paper defines action alignment through an intent- and consequence-based formulation and detects misaligned actions before execution. It distinguishes three categories according to violated conditions and uses a hybrid strategy to collect representative trajectories.

  • 2.1. Action Alignment: Action alignment is introduced as the central concept underlying the study.
  • 2.1. Action Alignment: An action is aligned when it serves the user instruction, avoids unauthorized or undesired consequences, and contributes reasonably to the intended task.
  • 2.1. Action Alignment: Alignment does not require optimality; inefficient, exploratory, or unsuccessful actions remain aligned when they genuinely attempt to advance the user’s goal.
  • 2.1. Action Alignment: Misaligned action detection determines whether a proposed action is misaligned from the instruction, history, observation, and action before execution.
  • 2.2. Categorization of Misaligned Actions: The three conditions yield a categorization based on which condition an action violates.
  • 2.2. Categorization of Misaligned Actions: The benchmark collection workflow generates trajectories both with external attacks and without attacks.
  • 2.2. Categorization of Misaligned Actions: Malicious Instruction Following occurs when agents follow malicious directives embedded in external environments instead of authentic user intent.
  • 2.2. Categorization of Misaligned Actions: Harmful Unintended Behavior causes unauthorized or harmful consequences through internal limitations, whereas task-irrelevant behavior undermines efficiency and reliability without direct harm.

3. MISACTBENCH: Comprehensive Evaluation for Misaligned Action Detection

MISACTBENCH is a realistic benchmark with human-annotated, action-level alignment labels designed to cover misaligned actions arising from external attacks and internal agent limitations. Its hybrid construction combines adversarial trajectories with controlled synthesis of internally arising deviations.

  • MISACTBENCH provides human-annotated, action-level binary alignment labels for realistic CUA interaction trajectories.
  • The benchmark uses a hybrid collection strategy to cover all three categories of misaligned actions at scale.
  • Raw Trajectory Collection: External-attack trajectories are drawn from adversarial benchmarks featuring vision-based environmental injections and diverse CUA behaviors.
  • Raw Trajectory Collection: Internally arising misalignments are added by selecting risk primitives, contextualizing them into concrete subgoals, and merging their actions into benign trajectories.
  • Annotation: Three independent annotators label each retained action as aligned or assign it to one of the three misalignment categories.
  • 558 trajectories and 2,264 annotated actions span all three misalignment categories.

4. DEACTION: Runtime Misaligned Action Detection and Correction

DEACTION is a plug-and-play runtime guardrail that intercepts proposed CUA actions before execution, analyzes their alignment, and iteratively guides revisions when needed. It combines efficient screening with structured reasoning and compact history summaries.

  • DEACTION evaluates each proposed action before execution using only actions and environment states, without requiring agent parameters or reasoning traces.
  • Misaligned Action Detection: The two-stage pipeline uses a lightweight fast check first and routes uncertain actions to systematic analysis to balance detection performance and latency.
  • Fast Check: The fast check conservatively approves actions that clearly correspond to necessary or canonical steps of the user intent.
  • Systematic Analysis: Systematic analysis inspects potential environmental injections and evaluates action alignment through structured components using the current context.
  • Context Management: Narrative summarization maintains compact history context while summaries are generated in parallel with agent execution, adding no detection latency.
  • Iterative Correction: When an action is flagged, structured feedback explains the problem and prompts revised actions in a closed loop until alignment or a retry limit is reached.

5. Experiments

DEACTION is evaluated offline on MISACTBENCH and online in interactive adversarial and benign environments. It improves detection and attack resistance while using history representations and routing choices that manage latency and computational cost.

  • Offline Evaluation: On MISACTBENCH, DEACTION reaches up to 89.9% precision and approximately 80% F1, outperforming prior approaches across backbones.
  • Offline Evaluation: Malicious instruction following is easiest to detect, whereas internally arising misalignment categories remain challenging to detect and classify.
  • History Representation: Narrative summaries achieve the best history-representation performance, while screenshots require 18s per step and over 1K tokens per screenshot versus 11s and 41 tokens for summaries.
  • Latency–Performance Tradeoff: The default two-stage configuration matches systematic analysis performance while reducing latency by bypassing heavy reasoning for clearly aligned actions.
  • Latency–Performance Tradeoff: Fast-check-only routing has the lowest latency but substantially degraded precision, consistent with its role as a conservative filter.
  • Online Evaluation: In online adversarial evaluation, DEACTION reduces attack success rate by over 90% across CUAs while improving task success under attack.
  • Online Evaluation: In benign OSWorld environments, task success remains comparable or sometimes improves, while DEACTION adds 7.2 seconds of guardrail reasoning to 28.1 seconds per step on average.
  • Cost-Efficient Deployment: Replacing the fast check and narrative summarizer with lighter models reduces cost by 65% while preserving comparable detection performance.

6. Related Work

Prior CUA research describes agents, studies deviations from user intent, and examines intent preservation. The paper positions misaligned-action detection as broader than safety-centric approaches focused on attacks, policies, or trajectory-level behavior.

  • Computer-Use Agents: CUAs automate digital workflows by translating high-level user instructions into low-level keyboard and mouse actions through graphical interfaces.
  • Misaligned Actions in Agent Execution: Prior work observes that CUA actions can deviate from user intent through environment-induced attacks, internal limitations, or task-irrelevant behavior.
  • Misaligned Actions in Agent Execution: Safety-centric guardrails are difficult to apply to non-malicious, task-irrelevant actions.
  • Intent Preservation: Related work studies user-intent preservation through task-mutation stress tests and synthesized inputs that expose planning errors.

7. Conclusion

The paper defines misaligned action detection for CUAs, introduces MISACTBENCH, and proposes DEACTION to detect and correct deviations before execution. DEACTION improves detection, reduces attack success under adversarial conditions, and preserves task success in benign environments.

  • DEACTION proactively detects misaligned actions before execution and corrects them through iterative feedback.
  • MISACTBENCH provides human-annotated action-level labels for studying misaligned actions in CUAs.
  • DEACTION improves misaligned action detection, reduces attack success rate under adversarial settings, and preserves task success rate in benign environments.

Impact Statement

This work targets safer and more reliable computer-use agents in open-ended, real-world environments by detecting and correcting actions that deviate from user intent. Its benchmark and runtime guardrail aim to reduce safety risks, unnecessary side effects, and execution-time failures.

  • The work studies misaligned action detection and proposes a runtime guardrail to improve action alignment during agent execution.
  • The primary goal is enhancing the reliability and robustness of computer-use agents operating in open-ended, real-world computer environments.
  • Detecting and correcting actions before execution aims to reduce safety risks, unnecessary side effects, and execution-time failures.

A. Limitations and Future Directions

The paper identifies limitations involving disguised instructions, GUI grounding, outcome prediction, intent reasoning, long-horizon drift, and fine-grained misalignment characterization. It also notes that some benchmark trajectories rely on controlled synthesis because internal failures are difficult to collect organically.

  • Well-disguised malicious instructions can bypass detection, while coordinate-based grounding and outcome prediction can produce errors.
  • Controlled synthesis is used because internally arising misaligned actions are rare and long-tailed in unconstrained benign execution.
  • Explicit modeling of long-horizon drift remains future work, especially for failures emerging after many low-risk actions.
  • Fine-grained misalignment classification remains challenging because the runtime guardrail primarily makes binary interception decisions.
  • The external-attack trajectories are constructed from existing benchmarks with vision-based environmental injections.
  • The construction uses a diverse set of CUAs, including GPT-4o, Claude Sonnet models, and OpenAI CUA.

B.2. Trajectory Synthesis without Attacks

The no-attack synthesis procedure creates realistic internally arising misalignments by grounding unintended subgoals in benign CUA trajectories, executing them, merging their actions, replaying the result, and manually validating quality. An example shows an unauthorized file deletion after successful task completion.

  • Motivation: Internally arising misaligned actions can result from erroneous reasoning, grounding failures, or misinterpreted task boundaries.
  • Motivation: 158 internally arising misaligned actions were identified, with Harmful Unintended Behavior accounting for one-sixth of them.
  • Synthesis pipeline: The procedure injects unintended subgoals and subtle harmful actions into benign trajectories while preserving realistic interaction patterns.
  • Synthesis pipeline: Unintended subgoal primitives represent high-level harmful objectives, which are grounded into concrete subgoals using task and environment context.
  • Synthesis pipeline: An auxiliary LLM selects and contextualizes a plausible unintended subgoal, then identifies a trajectory step for divergence.
  • Synthesis pipeline: The concrete subgoal is executed independently, and its necessary actions replace a benign action at the selected injection point.
  • Synthesis pipeline: The synthesized trajectory is replayed from the beginning so subsequent observations reflect environment changes caused by injected actions.
  • Validation: Manual validation checks execution, subgoal achievement, and trajectory coherence before retaining synthesized examples.

D. Details of Offline Experiments

The offline experiments adapt existing baselines to the computer-use setting and use a stratified subset of MisActBench for ablations.

  • Task Shield and InferAct receive prompt and input adjustments for computer-use scenarios, including task-background changes and screenshot inputs.Task Shield is evaluated only through its agent-output checking component.
  • Ablation experiments sample one quarter of MisActBench’s labeled action steps, stratified by trajectory source.The resulting subset contains 566 steps: 242 misaligned and 324 aligned actions.

E.1. Benchmarks

The online evaluation covers adversarial RedTeamCUA tasks and benign OSWorld tasks, using attack, utility, and task-success metrics to compare DEACTION with representative defenses. Results show strong detection, correction, practical latency, and cost-efficiency patterns, while error types differ across settings.

  • Benchmarks: RedTeamCUA evaluates 50 high-risk adversarial tasks with a 50-step maximum, while OSWorld uses its default configuration with a 15-step maximum.RedTeamCUA begins from a clean state and exposes agents to webpages containing injected instructions; no-defense OSWorld results use published leaderboard performance.
  • Metrics: Attack Success Rate measures successful attacker objectives, Utility under Attack measures preserved user intent, and OSWorld Success Rate measures task completion.Lower ASR is better, whereas higher UA and SR indicate better utility or task performance.
  • Detection and classification: DEACTION’s detection performance varies substantially by misalignment type, with malicious instruction following easiest to detect and other unintended behavior hardest.Malicious instructions provide explicit cues, whereas task-irrelevant behavior often lacks malicious or harmful signals.
  • Detection and classification: Malicious instruction following is easier to classify after detection, while unintended harmful behavior remains difficult to distinguish from other non-malicious deviations.Other task-irrelevant behavior has lower detection recall but high classification recall once flagged.
  • Efficiency: 7 seconds per step accounts for approximately 25% of total per-step execution time, indicating moderate guardrail overhead across settings.The reported reasoning time remains stable regardless of external attacks.
  • Intervention: 78% of misaligned actions are corrected through iterative feedback, with injected-instruction cases often fixed within one revision.OSWorld has a slightly lower overall misalignment fraction than RedTeamCUA because it lacks external attacks.
  • Cost and deployment: Replacing fast-check and narrative-summarizer models with GPT-5 mini reduces cost by 65% while maintaining or slightly improving offline detection performance.Replacing the systematic-analysis model with a smaller model causes a noticeable performance drop; online attack and task success rates remain comparable after downsizing lightweight components.

F.5. Error Analysis

The error analysis examines failures across DEACTION’s fast check and four systematic-analysis components. Errors arise from disguised injections, inaccurate UI grounding, faulty outcome models, and overly narrow intent-level reasoning.

  • Scope: The analysis covers five pipeline components: fast check, injection analysis, action understanding, outcome prediction, and misalignment analysis.Each stage is illustrated with a representative incorrect judgment.
  • Fast check: The fast check can approve misaligned actions when malicious instructions are disguised as benign task guidance.Its lightweight model and shallow reasoning may prevent deeper systematic analysis from being triggered.
  • Injection analysis: Injection analysis may treat malicious web instructions as benign when they mimic legitimate documentation and lack overt adversarial cues.This allows malicious-instruction-following actions to be approved.
  • Action understanding: Action understanding errors result from inaccurate grounding between screen coordinates and interface elements, causing incorrect interpretations of agent intent.The guardrail can therefore misread the semantic meaning of a coordinate-based action.
  • Outcome prediction: Outcome prediction fails when the language model’s internal world model produces incorrect expectations about an action’s effects.These erroneous expectations lead to incorrect alignment judgments.
  • Misalignment analysis: Misalignment analysis can falsely flag reasonable transitional actions when task relevance is interpreted too narrowly.Such back-to-track actions may help restore the correct workflow despite appearing indirectly relevant.
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