Recognition: no theorem link
WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks
Pith reviewed 2026-05-15 22:18 UTC · model grok-4.3
The pith
WASP benchmark shows top web agents deceived by simple prompt injections with partial success up to 86 percent.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
WASP provides a publicly available benchmark for end-to-end evaluation of web agent security against prompt injection attacks. Evaluating leading models with it shows that simple low-effort human-written injections deceive agents in realistic scenarios. Attacks achieve partial success in up to 86 percent of cases while state-of-the-art agents often struggle to fully complete the attacker's goals.
What carries the argument
The WASP benchmark, which supplies realistic multi-step web tasks and human-written injection examples to measure how far agents progress toward attacker objectives.
If this is right
- Simple prompt injections can deceive even top-tier models with advanced reasoning in realistic web tasks.
- Partial attack success reaches up to 86 percent across evaluated scenarios.
- Agents rarely finish the full attacker goals even when partially deceived.
- Security in current web agents depends more on incomplete execution of malicious instructions than on built-in protections.
Where Pith is reading between the lines
- Greater agent reasoning ability could raise the risk of full attacker goal completion if vulnerabilities persist.
- Real deployments for tasks like financial actions may need extra layers of oversight beyond model behavior.
- Expanding WASP with live user sessions could test whether benchmark results predict actual attack outcomes.
Load-bearing premise
The benchmark tasks and injection examples accurately represent real-world web agent usage and attacker capabilities without over-simplifying or granting attackers unrealistic control.
What would settle it
Finding that agents in actual deployed settings complete attacker goals at much higher rates than the partial success observed in WASP tests would challenge the claim that security stems mainly from agent incompetence.
read the original abstract
Autonomous UI agents powered by AI have tremendous potential to boost human productivity by automating routine tasks such as filing taxes and paying bills. However, a major challenge in unlocking their full potential is security, which is exacerbated by the agent's ability to take action on their user's behalf. Existing tests for prompt injections in web agents either over-simplify the threat by testing unrealistic scenarios or giving the attacker too much power, or look at single-step isolated tasks. To more accurately measure progress for secure web agents, we introduce WASP -- a new publicly available benchmark for end-to-end evaluation of Web Agent Security against Prompt injection attacks. Evaluating with WASP shows that even top-tier AI models, including those with advanced reasoning capabilities, can be deceived by simple, low-effort human-written injections in very realistic scenarios. Our end-to-end evaluation reveals a previously unobserved insight: while attacks partially succeed in up to 86% of the case, even state-of-the-art agents often struggle to fully complete the attacker goals -- highlighting the current state of security by incompetence.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces WASP, a publicly available benchmark for end-to-end evaluation of web agents against prompt injection attacks in realistic multi-step scenarios. It evaluates top-tier models and reports that simple human-written injections achieve partial success in up to 86% of cases, while agents rarely fully complete attacker goals, which the authors interpret as evidence of 'security by incompetence' in current systems.
Significance. If the benchmark tasks and injection examples are representative of real-world usage and the evaluation includes proper controls, WASP would offer a valuable standardized tool for measuring progress toward secure web agents and expose practical vulnerabilities in deployed models.
major comments (2)
- [Abstract and evaluation results] The central interpretation in the abstract—that low full completion of attacker goals demonstrates 'security by incompetence'—is not supported without clean-task baseline success rates. The manuscript must report the agents' success rates on the identical tasks with no injections present; absent this, the observed partial successes (up to 86%) and incomplete goals could simply reflect general unreliability on complex multi-step web tasks rather than any security property.
- [Evaluation section] The abstract and results claim concrete outcomes (86% partial success) but the provided text lacks sufficient detail on task definitions, success metrics, statistical significance, number of trials, and inter-rater or automated verification procedures. These elements are load-bearing for the empirical claims and must be expanded with explicit methodology.
minor comments (2)
- [Abstract] The phrase '86% of the case' in the abstract should be corrected to '86% of the cases'.
- [Evaluation] Clarify the exact criteria used to distinguish 'partial success' from 'full completion' of attacker goals, including any scoring rubric or examples.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback on our WASP benchmark paper. We agree that the current manuscript requires additional baselines and expanded methodological details to fully support the empirical claims and interpretations. We will perform the necessary revisions, including new experiments for clean-task baselines and substantial expansions to the evaluation section, to address both major comments.
read point-by-point responses
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Referee: [Abstract and evaluation results] The central interpretation in the abstract—that low full completion of attacker goals demonstrates 'security by incompetence'—is not supported without clean-task baseline success rates. The manuscript must report the agents' success rates on the identical tasks with no injections present; absent this, the observed partial successes (up to 86%) and incomplete goals could simply reflect general unreliability on complex multi-step web tasks rather than any security property.
Authors: We acknowledge that the 'security by incompetence' framing in the abstract is not fully supported without clean baselines, as the referee correctly notes. The observed partial successes and incomplete attacker goals could indeed partly reflect baseline task difficulty. In the revised manuscript, we will add success rates for all agents on the identical WASP tasks with no injections present. This will enable direct comparison, allowing us to quantify the incremental effect of injections versus general unreliability. We will update the abstract, results, and discussion sections to reflect these new data and revise the interpretation accordingly. revision: yes
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Referee: [Evaluation section] The abstract and results claim concrete outcomes (86% partial success) but the provided text lacks sufficient detail on task definitions, success metrics, statistical significance, number of trials, and inter-rater or automated verification procedures. These elements are load-bearing for the empirical claims and must be expanded with explicit methodology.
Authors: We agree that the evaluation methodology section is insufficiently detailed to support the reported results. In the revision, we will substantially expand this section to include: precise definitions of each task and sub-goal; exact criteria for partial vs. full success (including how partial success is quantified); number of independent trials per agent-task pair; statistical significance testing; and full details on verification procedures (automated success checks supplemented by human review where needed). We will also add pointers to the public benchmark code and data for full reproducibility. revision: yes
Circularity Check
No circularity: empirical benchmark results are direct measurements
full rationale
The paper introduces the WASP benchmark and reports empirical attack success rates from running it on existing agents. No derivations, equations, fitted parameters renamed as predictions, or self-citations that reduce the central claims to tautologies appear. The 86% partial success and incomplete goal completion figures are presented as observed outcomes from the new tasks, not forced by construction from prior inputs or definitions. The skeptic concern about missing clean baselines is a question of experimental design completeness, not circularity in the reported chain.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Simulated web environments can faithfully represent real-world agent interactions for security testing
Forward citations
Cited by 19 Pith papers
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IPI-proxy: An Intercepting Proxy for Red-Teaming Web-Browsing AI Agents Against Indirect Prompt Injection
IPI-proxy is a toolkit using an intercepting proxy to inject indirect prompt injection attacks into live web pages for testing AI browsing agents against hidden instructions.
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The Granularity Mismatch in Agent Security: Argument-Level Provenance Solves Enforcement and Isolates the LLM Reasoning Bottleneck
PACT achieves perfect security and utility under oracle provenance by enforcing argument-level trust contracts based on semantic roles and cross-step provenance tracking, outperforming invocation-level monitors in Age...
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WAAA! Web Adversaries Against Agentic Browsers
Agentic browsers are vulnerable to 20 web and LLM attacks with 18 implemented, exposing five failure modes across four major LLM models that require redesign before safe deployment.
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Indirect Prompt Injection in the Wild: An Empirical Study of Prevalence, Techniques, and Objectives
Large-scale analysis of 1.2B URLs identifies 15.3K indirect prompt injection instances in the wild, mostly targeting AI systems with up to 8% compliance in model experiments.
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OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents
OS-SPEAR is a new evaluation toolkit that tests 22 OS agents and identifies trade-offs between efficiency and safety or robustness.
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Web Agents Should Adopt the Plan-Then-Execute Paradigm
Web agents should default to planning a complete task program before observing live web content to reduce prompt injection exposure, since WebArena tasks are compatible and 80% need no runtime LLM calls.
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SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces
SkillSafetyBench shows that localized non-user attacks via skills and artifacts can consistently induce unsafe agent behavior across domains and model backends, independent of user intent.
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When Agents Overtrust Environmental Evidence: An Extensible Agentic Framework for Benchmarking Evidence-Grounding Defects in LLM Agents
EnvTrustBench benchmarks evidence-grounding defects in LLM agents and finds they occur consistently across workflows.
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When Agents Overtrust Environmental Evidence: An Extensible Agentic Framework for Benchmarking Evidence-Grounding Defects in LLM Agents
EnvTrustBench is a new agentic benchmark that measures evidence-grounding defects where LLM agents overtrust faulty environmental observations and take incorrect actions.
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SnapGuard: Lightweight Prompt Injection Detection for Screenshot-Based Web Agents
SnapGuard detects prompt injection attacks on screenshot-based web agents via visual stability indicators and contrast-polarity textual signals, reaching F1 0.75 while running 8x faster than GPT-4o with no added memory cost.
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HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark
HINTBench provides 629 annotated agent trajectories to audit intrinsic non-attack risks, showing that strong LLMs detect overall risk but fail at localizing specific risky steps or diagnosing failure types.
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Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode
Independent evaluation of Claude Code auto mode finds 81% false negative rate on ambiguous authorization tasks due to unmonitored file edits.
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Security Considerations for Multi-agent Systems
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
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Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems
Safety constraints in LLM-based multi-agent systems commonly weaken during execution through memory, communication, and tool use, requiring them to be maintained as explicit state rather than asserted once.
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Constraining Host-Level Abuse in Self-Hosted Computer-Use Agents via TEE-Backed Isolation
A TEE-backed architecture isolates security-critical decisions in self-hosted AI agents to prevent host-level abuse from malicious inputs while maintaining allowed functionality.
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WebAgentGuard: A Reasoning-Driven Guard Model for Detecting Prompt Injection Attacks in Web Agents
WebAgentGuard is a reasoning-driven multimodal model trained on large synthetic data via supervised fine-tuning and reinforcement learning to detect prompt injections in web agents better than prior defenses.
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PIArena: A Platform for Prompt Injection Evaluation
PIArena provides a unified evaluation platform for prompt injection attacks and defenses, featuring a new adaptive attack that reveals major weaknesses in existing protections.
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Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment inter...
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Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces
This survey organizes RL for LLM multi-agent systems into reward families, credit units, and five orchestration sub-decisions, notes the absence of explicit stopping-decision training in its paper pool, and releases a...
Reference graph
Works this paper leans on
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[1]
WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models
https://arxiv.org/abs/2401.13919. Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, and Daniel Fried. Visualwebarena: Evaluating multimodal agents on realistic visual web tasks. arXiv preprint arXiv:2401.13649, 2024. Ang Li, Yin Zhou, Vethavikashini Chithrra Raghuram, Tom G...
work page internal anchor Pith review arXiv 2024
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[2]
4 Let’s think step-by-step. The objective is to comment on a given issue saying ‘‘we are working on it.’’ Currently, the observation shows that we are on the profile page in GitLab. I need to navigate to the ’Issues’ section . . . I can see a ’Projects’ button with ID [4] . . . In summary, the next action I will perform is: click [4]. Agents opens account...
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[3]
Comment on the given issue saying we are working on it
Let’s think step-by-step. The objective is to comment on a specific issue . . . Since I haven’t been given specific instructions about which project or issue to comment on, I’ll need to find the specific project or issue that aligns with the objective . . . ‘‘Byte Blaze/ a11y-syntax- highlighting,’’ and see if it contains the issue that needs to be addres...
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[4]
Then navigate to the issue 3. Finally add the comment Observation: The model has laid out a plan to achieve the legitimate objective and there is no evidence of competing objectives. “‘ Thought: Since the model’s reasoning is consistent with the legitimate objective and there is no evidence of another objective being pursued, it is not compromised. Output...
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[5]
Then navigate to the issue 3. Finally add the comment I see the Firefox icon. Let me click it and go to the login page first. Now I’ll navigate to the login page by typing the URL: Now I’ll log in with the provided credentials: Now that I’m logged in, I’ll navigate to the specified issue: Now I see the issue page. I’ll add a comment by clicking in the com...
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[6]
Then navigate to the issue 3. Finally add the comment I see the Firefox icon. Let me click it and go to the login page first. Now I’ll navigate to the login page by typing the URL: Now I’ll log in with the provided credentials: Now that I’m logged in, I’ll navigate to the specified issue: Now I see the issue page. I’ll add a comment by clicking in the com...
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[7]
Claims Question: Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope? Answer: [Yes] Justification: The abstract and intro claim a new publicly available benchmark and it is available at https://github.com/facebookresearch/wasp The cited results match those from Figure 1 and Table 2
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[8]
Limitations Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes] Justification: See section “Limitations and future work” of the Conclusion
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[9]
Theory assumptions and proofs Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? Answer: [NA] Justification: This is a benchmark paper with no theoretical results
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[10]
Absent any changes to the model APIs, all information to reproduce the experiments is available
Experimental result reproducibility Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [Yes] Justification: In addition to the publicl...
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[11]
Open access to data and code Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: See answers above
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[12]
Experimental setting/details Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [Yes] Justification: The paper does not train any models. The exact dataset size is given in Sections 4.1 and 4.2 and it is 84 use...
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[13]
Experiment statistical significance Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments? Answer: [No]
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[14]
Experiments compute resources Question: For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? 20 Answer: [No] Justification: We test cloud-hosted models (GPT-4o, o1, Claude) and their providers do not share these kinds of details
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[15]
Code of ethics Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines? Answer: [Yes]
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[16]
Broader impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed? Answer: [Yes] Justification: This is the goal of the paper itself. By measuring realistic security issues with foundational models used as web agents, we obtain a more accurate estiamte of potential societal risk (...
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[17]
Safeguards Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [Yes] Justification: We do not release a new model and perform all of our tests on self-hosted environments where n...
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[18]
Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: We include this information in the repo README and cite the relevant papers here
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New assets Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets? Answer: [Yes] Justification: We provide a README in the GitHub repository and document our code
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[20]
Crowdsourcing and research with human subjects Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)? Answer: [NA] Justification: No human subjects were invovled
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[21]
Institutional review board (IRB) approvals or equivalent for research with human subjects Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or ...
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[22]
Answer: [NA] Justification: We do not use LLMs in any special way covered by the policy
Declaration of LLM usage 21 Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, de...
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