Cited by 5Adversarial Robustness in Machine LearningWeb Application Security VulnerabilitiesSecurity and Verification in Computing

Attacking Vision-Language Computer Agents via Pop-ups

Open access
Zhang, Yanzhe, Yu, Tao, Yang, Diyi·2025·Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Abstract

Autonomous agents powered by large vision and language models (VLM) have demonstrated significant potential in completing daily computer tasks, such as browsing the web to book travel and operating desktop software, which requires agents to understand these interfaces.Despite such visual inputs becoming more integrated into agentic applications, what types of risks and attacks exist around them still remain unclear.In this work, we demonstrate that VLM agents can be easily attacked by a set of carefully designed adversarial pop-ups 1 , which human users would typically recognize and ignore.This distraction leads agents to click these pop-ups instead of performing their tasks as usual.Integrating these pop-ups into existing agent testing environments like OSWorld and VisualWebArena leads to an attack success rate (the frequency of the agent clicking the pop-ups) of 86% on average and decreases the task success rate by 47%.Basic defense techniques, such as asking the agent to ignore pop-ups or including an advertisement notice, are ineffective against the attack.Code is available at https: //github.com/SALT-NLP/PopupAttack.

AI snapshot

Written from the paper’s full text.

TL;DR

Carefully designed adversarial pop-ups can reliably mislead VLM computer agents into clicking them, with an average attack success rate of 86% and a 47% decrease in task success rate across OSWorld and VisualWebArena. Basic prompt-based defenses are largely ineffective.

Key findings

  • Across five frontier VLMs, all models showed high attack success rates: Table 1 reports ASR >60% in all scenarios, e.g., OSWorld-Screen 93.3–100.0, OSWorld-SoM 88.7–95.3, and VisualWebArena-SoM 62.1–78.4. Figure 1 reports that on average 92.7% / 73.1% of all actions of attacked agents in OSWorld / VisualWebArena are clicks on the adversarial pop-ups.
  • Ablations show the attention hook derived from the real user query is essential: replacing it with alternatives decreases ASR by 61% relatively on average. Other information such as pop-up position and agent framework is relatively unnecessary. Changing “OK” to “ADVERTISEMENT” still leaves ASR >55%; an empty ALT descriptor drops ASR by 23.7 and 19.2 points; blank pop-ups reduce ASR to 2.4 / 3.7 / 0.0 across settings.
  • Basic defenses are weak: asking the agent to ignore pop-ups in the system prompt provides almost no mitigation, and a more specific defensive prompt decreases ASR by only about 25% relatively while SRs remain low. Step-wise prompt defense further reduces ASR but only marginally increases SR.
  • Task-level attack success rate (TASR) is generally positively associated with ASR and is usually multiple times higher than ASR when ASR is small (<0.2), indicating accumulated risk across multi-step tasks.

Methods

  • Threat model: attackers manipulate agent observations (screenshots and accessibility trees) by inserting adversarial pop-ups with four design elements: Attention Hook, Instruction, Info Banner, and ALT Descriptor. The paper tests screenshot agents and Set-of-Mark (SoM) agents backed by general-purpose VLMs.
  • Data and models: OSWorld uses 50 easy tasks with a 15-step limit; VisualWebArena uses a 72-easy-task subset from Wu et al. (2024) with a 30-step limit and only SoM agents. Five VLMs are tested: gpt-4-turbo-2024-04-09, gpt-4o-2024-05-13, gemini-1.5-pro-002, claude-3-5-sonnet-20240620, and claude-3-5-sonnet-20241022. Decoding temperature is set to 0.
  • Analysis: main comparisons report ASR, SR, and OSR; ablation studies vary attention hook, instruction, info banner, ALT descriptor, pop-up size, and blank pop-ups; defense experiments test system-prompt and step-wise prompt defenses; thought annotations manually label 50 successful-attack thoughts per agent type on OSWorld.

Limitations

  • Paper-stated: only closed-source models are tested, which hinders deeper understanding of why the attack works; the authors chose this because open-source models performed relatively poorly on computer-agent benchmarks.
  • Paper-stated: the work does not explore more advanced jailbreaking techniques such as optimizing the string inside pop-ups or making pop-ups more persuasive; it focuses on high-level design and component contribution.
  • Paper-stated and evident: no redirection is implemented after clicks, and clicks are ignored during execution for simplicity; tasks are selected easy subsets and are not guaranteed to be completed without attacks due to decoding randomness. Evident from the text: evaluation is limited to two benchmarks and selected easy tasks, and the default threat model assumes complete attacker information, though ablations relax some assumptions.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.18653/v1/2025.acl-long.411

Cites

0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

1 of 2 cited sources checked · 1 supported · 0 flagged

  • Findingreasoning

    Prompting the model to ignore the pop-ups almost provides no mitigation effects.

    “Surprisingly, prompting the model to ignore the pop-ups almost provides no mitigation effects.”
    Quote in the paperSubstantial claim
  • Findingown data

    Some successful attack examples do not mention any elements from the pop-ups but generate actions that implicitly follow the instructions.

    “More interestingly, we find some successful examples without mentioning any elements from the pop-ups but generating actions that implicitly follow the instructions (Figure 6).”
    Quote in the paperSubstantial claim
  • Findingown data

    Basic defense techniques, such as asking the agent to ignore pop-ups or including an advertisement notice, are ineffective against the attack.

    “Basic defense techniques, such as asking the agent to ignore pop-ups or including an advertisement notice, are ineffective against the attack.”
    • Also stated in: § 1 Introduction · plain
      “Basic defense strategies, such as asking the agent to ignore pop-ups in system prompts and adding an extra advertisement notice, cannot effectively mitigate the issue (decrease the ASR by no more than 25% relatively).”
    Quote in the paperSubstantial claim
  • Findingown data

    More than 50% of tested VisualWebArena tasks can be completed within five steps.

    “we find that more than 50% of the tested VisualWebArena tasks can be completed within five steps”
    Quote in the paperSubstantial claim
  • Findingown data

    TASR is usually multiple times higher than ASR when ASR is small (<0.2).

    “More surprisingly, TASR is usually multiple times higher than ASR when ASR is small (< 0.2)”
    Quote in the paperSubstantial claim
  • Classified as probably not a substantial claim. 3
  • May combine two claims. 13
  • The quote only partly states this claim. 1
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The study does not explore more advanced jailbreaking techniques, such as optimizing the string inside the pop-ups or making the pop-ups more persuasive.

    “(ii) We do not explore more advanced jailbreaking techniques, such as optimizing the string inside the pop-ups (Zou et al., 2023) or making our pop-ups more persuasive (Zeng et al., 2024), but focus more on the high-level design of the adversarial pop-ups and the contribution analysis of different components.”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites Zou et al., 2023 source
    • cites Zeng et al., 2024 source
  • May combine two claims.

    Findingreasoning

    Both types of agents show increased robustness when the attack is delayed till after the 7th step, with ASR decreased to 80.4% for screenshot agents and 88.1% for SoM agents.

    “Both types of agents show increased robustness in this setting (ASR decreased to 80.4% for screenshot agents and 88.1% for SoM agents).”
    Quote in the paperSubstantial claim
  • May combine two claims.

    Findingown data

    OSWorld tasks usually start at an initial stage and involve more steps to explore the environment and complete the task.

    “In contrast, OSWorld tasks usually start at an initial stage and involve more steps to explore the environment and complete the task”
    Quote in the paperSubstantial claim
  • May combine two claims.

    Findingown data

    Integrating adversarial pop-ups into OSWorld and VisualWebArena leads to an average attack success rate of 86% and decreases task success rate by 47%.

    “Integrating these pop-ups into existing agent testing environments like OSWorld and VisualWebArena leads to an attack success rate (the frequency of the agent clicking the pop-ups) of 86% on average and decreases the task success rate by 47%.”
    Quote in the paperSubstantial claim
  • May combine two claims.

    Findingown data

    The correlation between ASR and Task-level Attack Success Rate (TASR) shows that TASR is generally similar to ASR and tends to be higher than ASR when ASR is low.

    “The correlation between ASR and Task-level Attack Success Rate (TASR) shows that TASR is generally similar to ASR and tends to be higher than ASR when ASR is low.”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Findingown data

    The benign template leads to a slight increase in SR and a slight decrease in ASR.

    “The benign template leads to a slight increase in SR and a slight decrease in ASR.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given

    There is no substantial improvement from GPT-4-Turbo to the updated Claude 3.5 Sonnet.

    “There is also no substantial improvement from GPT-4-Turbo to the updated Claude 3.5 Sonnet.”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Findingnone given

    Agents cannot distinguish the difference between pop-ups and typical digital content, even though the pop-ups look very suspicious to human users.

    “Even though these pop-ups look very suspicious (by design) to human users, agents cannot distinguish the difference between pop-ups and typical digital content.”
    Quote in the paperSubstantial claim
  • May combine two claims. The quote only partly states this claim.

    Findingnone given

    Agents declare WAIT/FAIL/DONE based on interaction history when they believe they solve the tasks, or tasks are not solvable.

    “This happens when agents believe they solve the tasks, or tasks are not solvable.”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Findingown data

    Without attack, agent thoughts tend to be more abstract without details and consider more diverse actions.

    “Without attack, the thoughts tend to be more abstract without details (example 1) and consider more diverse actions (example 2).”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Findingown data

    User query is essential for the attention hook; using other alternatives decreases ASR by 61% relatively on average.

    “(1) User query is essential for the attention hook, as using other alternatives (e.g., attackers speculate the user intent from the screen content.), on average, decreases the ASR by 61% relatively.”
    Quote in the paperSubstantial claim
  • May combine two claims.

    Findingown data

    The agent generates correct thoughts while its actions are implicitly affected by the pop-up instruction.

    “Interestingly, the agent generates correct thoughts while the actions are implicitly affected by the pop-up instruction.”
    Quote in the paperSubstantial claim
  • May combine two claims.

    Interpretationown data

    The initial state of VisualWebArena tasks is very close to the desired final state, and the agent only needs a few correct actions to succeed even if it clicks pop-ups most of the time.

    “suggesting the initial state is very close to the desired final state, and the agent only needs to take a few correct actions to succeed even they might click on the pop-ups most of the time.”
    Quote in the paperSubstantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The tag ID for the pop-up is randomly selected and does not correlate with its website position.

    “We randomly select a tag ID for our pop-up, which does not correlate with its website position.”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Interpretationown data

    The attack is generalizable and not only works for specific tasks.

    “suggesting our attack is generalizable and not only works for specific tasks.”
    Quote in the paperSubstantial claim
15 of 15
  • The authors state this as an assumption.

    In the default setting, the attacker has complete information, including the user query, the pop-up's position, and the underlying agent framework.

    “where we assume the attacker has complete information (including the user query, the pop-up's position, and the underlying agent framework, etc).”
    “assuming the attacker has all the needed information and takes the most”
  • The authors state this as an assumption.

    Text-based safety training prevents SoM agents from interacting with the pop-ups.

    “we assume textbased safety training prevents SoM agents from interacting with the pop-ups.”
  • The authors state this as an assumption.

    Our attack remains similarly effective as long as the texts within pop-ups are readable.

    “We assume our attack remains similarly effective as long as the texts within pop-ups are readable.”
  • The authors state this as an assumption.

    The inability of agents to distinguish pop-ups from other elements causes prompting the model to ignore pop-ups to provide almost no mitigation.

    “We assume this is due to the inability of agents to distinguish popups from other elements.”
  • The paper relies on this without support.

    The ground truth user query is tricky to access from the attacker perspective in real scenarios.

    “However, the ground truth user query is tricky to access from the attacker perspective in real scenarios”
  • The paper relies on this without support.

    The exact position of pop-ups might sometimes be out of control for the attackers.

    “However, the exact position of pop-ups might sometimes be out of control for the attackers.”
  • The paper relies on this without support.

    The tag ID is usually generated from the agent framework, where attackers have zero knowledge.

    “At the same time, the tag ID is usually generated from the agent framework, where attackers have zero knowledge.”
  • The paper relies on this without support.

    Attackers usually know other information on the screen while inserting pop-ups.

    “Attackers usually know other information on the screen while inserting pop-ups.”
  • The paper relies on this without support.

    As long as the environment functions well and human users can complete the tasks as usual, the agent should be able to complete the tasks as well.

    “As long as the environment functions well and human users can complete the tasks as usual, the agent should be able to complete the tasks as well.”
  • The authors state this as an assumption.

    The presence of summarized queries in the ALT descriptor plays a role in strengthening its importance from the SoM agent's perspective.

    “We assume the presence of summarized queries in the ALT descriptor plays a role in strengthening its importance from the SoM agent's perspective.”
  • The authors state this as an assumption.

    The pop-ups are clickable items.

    “which assumes the pop-ups are clickable items.”
  • The paper relies on this without support.

    Open-source models have relatively low performance on computer agent benchmarks.

    “This choice was made due to the relatively low performance of open-source models on computer agent benchmarks.”
12 of 12

Cites

The publisher hasn’t deposited this paper’s reference list.

Research works printed but not linked: 2

  1. Federal Trade Commission et al. 2013. . com disclosures: how to make effective disclo- sures in digital advertising. March, http://www. ftc. gov/sites/default/files/attachments/press- releases/ftc-staff-revises-online-advertising- disclosureguidelines/130312dotcomdisclosures. pdf.
  2. Yuta Imamura, Rintaro Orito, Kritsana Chaikaew, Célia Manardo, Pattara Leelaprute, Masaya Sato, and Toshihiro Yamauchi. 2019. Threat analysis of fake virus alerts using webview monitor. In 2019 Sev- enth International Symposium on Computing and Networking (CANDAR), pages 28-36. · DOI

Cited by

Sorted by citations

Authors
Zhang, Y., Yu, T., & Yang, D.
Affiliations
  • Georgia Institute of Technology (US)
  • University of Hong Kong (HK)
  • Stanford University (US)
Venue
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Published
2025
Pages
8387-8401
ISSN
0736-587X (ISSN-L)
OpenAlex
W4412945641
Cited by
4
References
0
Citations by year
2026: 3 · 2025: 1
FWCI
3.83
Type
conference paper
Language
en
Open access
Locations
  • Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Office of Naval Research
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Information Systems › Web Application Security Vulnerabilities
  • Physical Sciences › Computer Science › Artificial Intelligence › Security and Verification in Computing
Keywords
vision-language models, computational agents, pop-ups, adversarial attacks, large language models

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