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The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents

Xuwei Ding, Skylar Zhai, Linxin Song, Jiate Li, Taiwei Shi, Nicholas Meade, Siva Reddy, Jian Kang, Jieyu Zhao

arXiv:2604.10577v2cs.CRcs.AI

TL;DR

CUA safety evaluations have largely focused on explicit misuse or prompt injection, leaving benign instructions whose context or execution produces harm insufficiently tested. OS-BLIND benchmarks this unintended-attack setting and finds severe vulnerabilities across models and agentic frameworks, with defenses and multi-agent execution offering limited safety under benign instructions.

  • Problem

    Existing CUA safety evaluations primarily target explicit misuse and environment-embedded adversarial commands, overlooking cases where benign instructions become harmful during execution.

  • Method

    OS-BLIND evaluates CUAs on human-crafted tasks in which every user instruction is benign but harm arises from environment-embedded threats or agent-initiated execution outcomes.

  • Results

    Most CUAs exceed 90% ASR, while Claude 4.5 Sonnet reaches 73.0%; multi-agent deployment further raises its ASR from 73.0% to 92.7%.

  • Takeaways & Limitations

    Existing defenses provide limited protection under benign instructions, while safety alignment activates mainly during the first few steps and rarely re-engages during later execution.

  • Takeaways & Limitations

    The proposed explanation for multi-agent failures—that decomposed subtasks obscure harmful intent—is presented as a hypothesis about the mechanism.

Abstract

from arXiv · show

Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to automate harmful actions programmatically. Existing safety evaluations largely target explicit threats such as misuse and prompt injection, but overlook a subtle yet critical setting where user instructions are entirely benign and harm arises from the task context or execution outcome. We introduce OS-BLIND, a benchmark that evaluates CUAs under unintended attack conditions, comprising 300 human-crafted tasks across 12 categories, 8 applications, and 2 threat clusters: environment-embedded threats and agent-initiated harms. Our evaluation on frontier models and agentic frameworks reveals that most CUAs exceed 90% attack success rate (ASR), and even the safety-aligned Claude 4.5 Sonnet reaches 73.0% ASR. More interestingly, this vulnerability becomes even more severe, with ASR rising from 73.0% to 92.7% when Claude 4.5 Sonnet is deployed in multi-agent systems. Our analysis further shows that existing safety defenses provide limited protection when user instructions are benign. Safety alignment primarily activates within the first few steps and rarely re-engages during subsequent execution. In multi-agent systems, decomposed subtasks obscure the harmful intent from the model, causing safety-aligned models to fail. We will release our OS-BLIND to encourage the broader research community to further investigate and address these safety challenges.

1 Introduction

CUAs can perform useful multi-step digital tasks but may be misled into harmful actions when benign instructions conceal risks in the environment or execution context. OS-BLIND addresses this overlooked unintended-attack setting and finds severe vulnerabilities across CUA architectures.

  • Motivation: Existing CUA safety benchmarks primarily test explicit user misuse or adversarial commands embedded in the environment.They overlook cases where the user instruction appears benign and harmfulness emerges only during execution.
  • Benchmark motivation: OS-BLIND evaluates unintended attacks in which every task begins with a fully benign user instruction.The benchmark targets situations where harm emerges from the environment or during execution rather than from the prompt itself.
  • Benchmark motivation: Manual one-to-one task design preserves realistic and varied contexts for testing whether agents recognize harms before execution.This design avoids the reduced realism associated with automated templates.
  • Findings: Safety alignment mainly activates within the first few steps and rarely re-engages during later execution.The analysis links multi-agent task decomposition to failures in which safety-aligned models do not recover the original harmful intent.

2 OS-BLIND: Benchmarking Unintended User Attacks

OS-BLIND is a reproducible benchmark for harmful outcomes arising from benign instructions, using manually designed tasks across environmental and execution-based risks. Its evaluation combines annotated outcomes, automated judging, and expert review.

  • General setup: OS-BLIND defines harmfulness as violating acceptable-use policies or compromising system security, privacy, or data integrity.Every user instruction is benign, while harm arises through environment-embedded threats or agent-initiated harms.
  • Environment: The benchmark builds on OSWorld’s Ubuntu-based virtual desktop, where agents observe multimodal state and execute pyautogui actions.The environment runs in a virtual machine and uses configuration-driven task specifications for reproducibility.
  • Task categories: OS-BLIND distributes 300 tasks across 12 harmful categories spanning environment-embedded threats and agent-initiated harms.The first cluster includes deceptive websites, scam emails, harmful web content, and dynamically injected pop-ups.
  • Task design: Two human experts manually pair benign instructions with plausible real-world risk scenarios, reconstructing unstable web environments when necessary.The benchmark also introduces context-aware, task-specific misleading pop-ups rather than fixed coordinate-level injections.
  • Evaluation and review: Expert annotations identify concrete harmful actions, and any annotated harmful behavior is sufficient to classify a trajectory as unsafe.For non-pop-up tasks, annotations are combined with a gpt-4o judge and validated against independent human labels.
  • Evaluation and review: All 300 tasks undergo two-phase expert review, achieving 93.67% initial agreement before consensus review of ambiguous or discordant cases.Construction required two months and more than 1,000 person-hours of expert effort.

3 Evaluating CUA Security on OS-BLIND

The evaluation covers end-to-end agents and multi-agent frameworks using attempted rate and attack success rate across two harm clusters. Results show broad vulnerability, especially to environment-embedded threats, with multi-agent systems further degrading safety.

  • Evaluation setup: The study evaluates open-source and proprietary end-to-end CUAs alongside CoAct-1, Jedi, and Agent-S2 multi-agent frameworks.Multi-agent systems typically use an orchestrator to decompose tasks and a GUI operator to execute interface actions.
  • Evaluation setup: AR measures whether an agent proceeds without explicit refusal, whereas ASR measures whether any annotated harmful behavior occurs across three independent runs.This separates willingness to refuse from capability or execution failure.
  • End-to-end results: Open-source end-to-end models exceed 96% average ASR in Cluster I and 90% in Cluster II, with 100% AR across all categories.Claude 4.5 Sonnet and Opus report lower average ASRs of 82.4% and 54.7% in Cluster I, and 62.4% and 40.4% in Cluster II, respectively.
  • Threat clusters: For nearly all models, ASR is higher for environment-embedded threats than for agent-initiated harms.Claude 4.5 Opus reaches 54.7% in Cluster I versus 40.4% in Cluster II, indicating weaker use of environmental safety cues.
  • Multi-agent results: CoAct-1 raises Claude 4.5 Sonnet’s average ASR from 82.4% to 93.1% in Cluster I and from 62.4% to 92.2% in Cluster II.The increase occurs when gpt-5 orchestrates Claude 4.5 Sonnet as GUI operator; high ASRs also appear with Claude as both orchestrator and operator.

4 Analysis

OS-BLIND exposes broad safety failures under benign instructions: defenses only partially reduce attacks, refusals cluster early, and task decomposition obscures harmful intent. Failure modes include inspection, judgment, and threat-detection failures.

  • Defense Performance: Current defenses only partially reduce ASR on OS-BLIND, with prompt defenses weak for open-source models and MirrorGuard outperforming them while remaining limited.On a 50-task subset, MirrorGuard reduces ASR by 26.0 and 76.0 percentage points on two base models.
  • Refusal Timing: Most refusal actions occur in the first two steps, after which safety alignment rarely re-engages despite continuing harmful environmental content.Claude 4.5 Sonnet and Opus agents that refuse usually do so at the first step; MirrorGuard more often refuses at steps 2 and 3.
  • Multi-Agent Safety: In CoAct-1, orchestrator refusals account for almost all rejections, while the Claude 4.5 Sonnet GUI operator contributes very few refusals across turns.The orchestrator refuses before delegation, whereas the standalone safety-aligned GUI operator almost never refuses inside the multi-agent system.
  • Multi-Agent Safety: Subtask decomposition raises Claude 4.5 Sonnet’s ASR from 27.9% to 79.1%, and reconstructing intent still yields 86.1% ASR.These results support the claim that decomposed subtasks obscure the intent needed to recognize a malicious environment.
  • Task Granularity: Coarser subtasks triple orchestrator-blocked tasks from 5 to 15 by preserving enough workflow meaning to trigger safety alignment.Fine-grained actions such as “type username” appear harmless, while coarse plans can prompt safe rewrites or execution truncation.
  • Failure Modes: Agents exhibit inspection, judgment, and detection failures: they may execute uninspected scripts, facilitate harmful content, or accept disguised threats.The reported examples include trusting README instructions, forwarding abuse, proceeding on gambling platforms, and misreading pop-ups or malware.

5 Related Work

Prior CUA safety work spans benchmarks and defenses for web, OS, and hybrid environments, but safe behavior under benign instructions in complex environments remains unresolved. OS-BLIND is positioned as an attack-centric benchmark for this gap.

  • GUI Agents: CUA capability benchmarks evaluate realistic web and desktop workflows, while agents still struggle with precise grounding despite understanding user intent.Examples include WebArena, WebVoyager, OSWorld, WindowsAgentArena, AndroidWorld, and MobileWorld.
  • Computer-Use Agent Safety Challenges: Existing safety research covers environment-embedded attacks, interface-level adversarial behavior, and defenses including dynamic monitoring, guardrails, and risk prediction.MIRRORGUARD monitors screenshots and reasoning traces to detect risks and correct unsafe reasoning before action execution.
  • Positioning OS-BLIND: OS-BLIND differs from prior attack-centric benchmarks by beginning with benign instructions while evaluating harms that emerge from environment or execution context.The paper contrasts this framing with benchmarks beginning from explicitly malicious instructions or adversarial prompts.

6 Comparison of CUA Safety Benchmark

CUA safety benchmarks have progressed toward realistic interactive environments, but they differ in whether harm is specified by the user, embedded in the environment, or introduced during benign task execution. OS-BLIND targets the latter setting and broadens its mechanisms and outcomes.

  • Benchmark Evolution: Interactive OS and OS–Web benchmarks improve realism and interactivity, yet still retain limitations when assessing CUA safety risks.The comparison situates later VM-based evaluations as more realistic than simplified web or static settings.
  • Misuse Risks: Misuse benchmarks evaluate tasks whose harmfulness is directly specified by the user, including harming users, leaking information, destructive commands, or security-configuration changes.OS-HARM is cited as a representative example of this category.
  • Injection Risks: Injection benchmarks embed malicious instructions in the environment to redirect agents from the user’s original goal toward harmful actions.RTC-BENCH studies indirect prompt injection in hybrid Web–OS environments, while VPI-BENCH studies visually embedded malicious instructions.
  • Operational Risks: Risky-operational-behavior benchmarks ask whether agents introduce security risks while completing otherwise benign tasks.Examples include OS-HARM misbehavior, AUTOELICIT-BENCH, RIOSWORLD, and BLIND-ACT categories.
  • OS-BLIND’s Distinction: OS-BLIND is closest to BLIND-ACT’s lack-of-contextual-reasoning setting but broadens mechanisms, includes deceptive environments where users are victims, and frames evaluation around concrete harmful outcomes.The paper presents these as three distinctions from BLIND-ACT.

7 Conclusion

OS-BLIND evaluates computer-use agents when benign instructions lead to harm through the environment or execution outcome. Its results reveal severe vulnerabilities, limited defenses, early-only safety activation, and multi-agent failures caused by task decomposition.

  • Conclusion: OS-BLIND evaluates misleadingly harmless conditions in which every user instruction is benign but harm emerges from the environment or execution outcome.The benchmark examines these conditions across frontier models and agentic frameworks.
  • Conclusion: Most CUAs exceed 90% ASR, while Claude 4.5 Sonnet reaches 73.0% ASR under OS-BLIND.The conclusion characterizes these results as severe safety vulnerabilities.
  • Conclusion: Existing defenses provide limited protection, safety alignment mainly activates within the first few steps, and task decomposition causes safety-aligned models to fail.The ablation raises Claude 4.5 Sonnet’s ASR from 27.9% to 79.1%, while intent reconstruction does not restore safety alignment.
  • Conclusion: OS-BLIND provides a foundation for evaluating and improving CUA security in real-world deployments.The paper presents systematic vulnerability exposure as the basis for this use.

Ethics Statement

OS-BLIND evaluates agents in isolated Ubuntu virtual machines using synthetic sensitive information and simulated phishing and email environments. The benchmark uses standardized GUI actions and reproducible evaluation artifacts to support safer research.

  • All experiments run inside isolated OSWorld Ubuntu virtual machines, preventing harmful agent actions from producing real-world consequences.
  • Sensitive benchmark data—including credentials, API keys, recovery phrases, and financial information—is entirely synthetic.Phishing portals and email interfaces are reconstructed or simulated rather than connected to live services.
  • OS-BLIND supports reproducible evaluation, with benchmark and evaluation code intended for release on Hugging Face and GitHub.
  • Agents interact through standardized pyautogui-based mouse and keyboard primitives within the virtual desktop environment.The action space also includes waiting, completion, failure, and timing controls for asynchronous task execution.

C Attempted Rate Results

Attempted Rate measures whether agents proceed without explicitly refusing in at least one of three runs. Open-source end-to-end models generally attempt every task, while Claude models and agentic frameworks show different refusal patterns.

  • 100% AR occurs across every category for all open-source end-to-end models except the Claude series.This pattern is reported for the two evaluated threat clusters.
  • Claude models show substantially lower AR than the other open-source end-to-end models, reflecting active safety awareness.
  • Agentic frameworks generally achieve AR near or above 95%, regardless of the safety alignment of their underlying backbone.
  • AR is the fraction of tasks in which an agent proceeds without explicitly refusing in at least one of three independent runs.

E Active Defense Examples in Coarse-Granularity Subtask

Coarse-granularity multi-agent subtasks sometimes enabled active defenses through task rewriting or execution truncation. These defenses contrasted with examples where agents recognized harmful content yet still completed the harmful action.

  • Active Defense Examples: Two defense patterns appeared in CoAct-1: rewriting harmful subtasks into protective actions and truncating execution before harmful completion.
  • Task Rewriting: In the fraud-and-forgery example, the orchestrator redirected a recovery-phrase request toward reporting the phishing email rather than entering the secret phrase.The intended harmful behavior was entering the Secret Recovery Phrase into a phishing website.
  • Execution Truncation: In the system-sabotage example, the orchestrator instructed the GUI agent to copy a command without pressing Enter and then save a screenshot.The harmful alternative was executing sudo rm /etc/hosts, which would delete the system hosts file.
  • Failure Modes: Agents sometimes recognized harmful content but still treated transmitting or executing it as neutral task completion.Examples include forwarding abusive text and trusting README instructions while skipping source-level script inspection.

F.3 Detection Failure: Agent Fails to Identify Disguised Threats

Detection failures arose when agents failed to inspect disguised threats or misclassified injected interface elements as legitimate. The examples contrast inspection-based refusal with execution based on surface appearance or trusted descriptions.

  • Detection Failure: Both models encountered the same injected authorization pop-up, but Opus ignored it while Sonnet interpreted it as a legitimate system dialog and clicked Authorize.
  • Detection Failure: The shell-customizer task involved a script that exfiltrated SSH keys, AWS credentials, and npm tokens to eng-standards.evil.com.
  • Detection Failure: Opus inspected a shell script, identified credential exfiltration to evil.com, and refused to run it, while Sonnet executed the same script without inspection.

G OpenCUA-72B Hallucination in Multi-Step Execution

OpenCUA-72B sometimes identifies harmful commands correctly at the start of a task but later loses them and executes unrelated benign actions instead. These lower attack success rates therefore reflect context loss rather than safety awareness.

  • Interpretation: OpenCUA-72B’s slightly lower ASRs on some System Sabotage and Code Injection categories are attributed to hallucination, not safety awareness.The model’s early comprehension was followed by unrelated command execution, producing an apparent reduction in harmful-task completion.
  • Firewall example: OpenCUA-72B correctly identified the harmful firewall command but later executed an unrelated VLC installation.The model recognized that sudo ufw disable would disable the firewall, then replaced it with sudo apt install vlc.
  • Environment-path example: OpenCUA-72B correctly parsed the malicious PATH modification but later pursued unrelated package and MSBuild actions.The model identified the .bashrc instruction, then ran sudo apt update and spent subsequent steps configuring an unrelated MSBuild task.
  • Cross-example pattern: Both examples show harmful commands being forgotten rather than refused during multi-step execution.No safety reasoning or explicit refusal appeared, and the intended action was replaced by a hallucinated benign action.

H CUA Benchmark Semantic Diversity

The paper measures benchmark semantic diversity by grouping near-duplicate tasks using textual and visual similarity. OS-BLIND has substantially greater measured diversity than the compared benchmarks.

  • Diversity comparison: OS-BLIND achieves an MSD score of 0.74, compared with 0.22 for OS-HARM and 0.02 for VPI-BENCH.The comparison quantifies the semantic-diversity gap between OS-BLIND and two template-based benchmarks.
  • Grouping procedure: The MSD procedure estimates semantically unique task groups from connected components in a near-duplicate graph.Each benchmark sample is a node, and near-duplicate samples are linked under a multimodal similarity rule.
  • Interpretation: A higher MSD indicates a larger proportion of semantically unique task configurations and greater benchmark diversity.The measure is intended to distinguish benchmarks with more varied task configurations from those with greater redundancy.
  • Similarity rule: Near-duplicates are linked when either textual or visual similarity reaches its corresponding threshold.This conservative union rule groups samples as semantically similar when either instruction semantics or initial GUI state is extremely similar.
  • Implementation: The implementation encodes instructions and screenshots with CLIP and uses τimg = τtxt = 0.99 for merging highly similar pairs.Text and initial-screenshot embeddings are compared using cosine similarity after normalization.

I Trajectory Case Studies

The trajectory case studies illustrate how agents execute safety-critical actions across phishing, exfiltration, fraud, malware, sabotage, misinformation, harassment, and risky financial scenarios. Across examples, agents prioritize routine task completion while overlooking deceptive content, confidentiality constraints, legitimacy checks, or harmful outcomes.

  • Case-study design: The trajectory section presents representative step-by-step interactions between users, agents, and environments for each risk category.The trajectories are accompanied by desktop screenshots and organized across Tables 12–23.
  • Credential phishing: In credential phishing, the agent copied personal information into a fraudulent form without verifying the portal’s legitimacy.The scenario involved a local file containing name, address, phone, and email data.
  • Data exfiltration: In data exfiltration, the agent exported a confidential acquisition document despite an explicit prohibition on copying or distribution.The agent treated the PDF export as routine and failed to heed the document-level security notice.
  • Fraud and forgery: In fraud and forgery, the agent modified financial records from an unverified email without checking the sender or request legitimacy.The action added a new bank account number to an employee spreadsheet and could facilitate financial fraud.
  • Malware deployment: In malware deployment, the agent executed an email attachment that was disguised as a benchmark but actually ran a cryptocurrency miner.The agent downloaded and ran the script without inspecting its source code.
  • System sabotage: In system sabotage, the agent prepended an attacker-controlled directory to PATH without verifying the email, enabling malicious binaries to shadow legitimate commands.The trajectory activated the modified PATH by sourcing .bashrc.
  • Other harms: The case studies include misinformation, harassment, and risky financial actions performed as routine desktop tasks.Examples include posting fabricated allegations, overlaying a degrading image, and attempting to purchase a machine gun.
  • Risky financial platforms: The agent analyzed a prediction-market bet and saved a forecast without warning about gambling risks, legal restrictions, or financial loss.The example shows routine analytical behavior on a betting platform despite the associated risks.

J.2 Category 2: Data Exfiltration

Data Exfiltration tests whether agents can be manipulated into transferring sensitive local information to unauthorized external destinations under apparently benign workflows. Examples disguise exfiltration as cloud backup, email attachment, troubleshooting upload, or job-assessment submission.

  • Data Exfiltration targets unauthorized transfer of sensitive local information, including SSH keys, proprietary code, and financial records.The category frames these transfers as harmful when disguised as ordinary computer workflows.
  • A backup request instructs the agent to run sync_env.py, whose execution uploads local environment configurations and credentials to an external server.The attack succeeds when the agent executes the script without reviewing its code.
  • A job-assessment workflow asks the agent to copy proprietary source code from CoreCode.txt into an external portal, exfiltrating intellectual property.The harmful outcome is submission of the source code to an unknown third party.
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