WebAgentGuard: A Reasoning-Driven Guard Model for Detecting Prompt Injection Attacks in Web Agents

• URL: http://arxiv.org/abs/2604.12284v1
• Date: Tue, 14 Apr 2026 04:50:35 GMT
• Title: WebAgentGuard: A Reasoning-Driven Guard Model for Detecting Prompt Injection Attacks in Web Agents
• Abstract summary: Web agents are highly vulnerable to prompt injection attacks.<n>Existing defenses, including system prompt defenses and direct fine-tuning of agents, have shown limited effectiveness.<n>We introduce WebAgentGuard, a reasoning-driven, multimodal guard model for prompt injection detection.
• Score: 117.65855863464863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Web agents powered by vision-language models (VLMs) enable autonomous interaction with web environments by perceiving and acting on both visual and textual webpage content to accomplish user-specified tasks. However, they are highly vulnerable to prompt injection attacks, where adversarial instructions embedded in HTML or rendered screenshots can manipulate agent behavior and lead to harmful outcomes such as information leakage. Existing defenses, including system prompt defenses and direct fine-tuning of agents, have shown limited effectiveness. To address this issue, we propose a defense framework in which a web agent operates in parallel with a dedicated guard agent, decoupling prompt injection detection from the agent's own reasoning. Building on this framework, we introduce WebAgentGuard, a reasoning-driven, multimodal guard model for prompt injection detection. We construct a synthetic multimodal dataset using GPT-5 spanning 164 topics and 230 visual and UI design styles, and train the model via reasoning-intensive supervised fine-tuning followed by reinforcement learning. Experiments across multiple benchmarks show that WebAgentGuard consistently outperforms strong baselines while preserving agent utility, without introducing additional latency.

Related papers

• WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections [85.68545794960214]
  WARD (Web Agent Robust Defense against Prompt Injection) is a practical guard model for secure and efficient web agents.<n>WARD is built on WARD-Base, a large-scale dataset with around 177K samples collected from 719 high-traffic URLs and platforms.<n>We introduce A3T, an adaptive adversarial attack training framework that iteratively strengthens WARD through a memory-based attacker and guard co-evolution process.
  arXiv  Detail & Related papers  (2026-05-14T16:26:27Z)
• It's a TRAP! Task-Redirecting Agent Persuasion Benchmark for Web Agents [52.81924177620322]
  Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking.<n>Their reliance on dynamic web content makes them vulnerable to prompt injection attacks: adversarial instructions hidden in interface elements that persuade the agent to divert from its original task.<n>We introduce the Task-Redirecting Agent Persuasion Benchmark (TRAP), an evaluation for studying how persuasion techniques misguide autonomous web agents on realistic tasks.
  arXiv  Detail & Related papers  (2025-12-29T01:09:10Z)
• BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents [8.923854146974783]
  We examine the landscape of prompt injection attacks and synthesize a benchmark of attacks embedded in realistic HTML payloads.<n>Our benchmark goes beyond prior work by emphasizing injections that can influence real-world actions rather than mere text outputs.<n>We propose a multi-layered defense strategy comprising both architectural and model-based defenses.
  arXiv  Detail & Related papers  (2025-11-25T18:28:35Z)
• IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents [33.775221377823925]
  Large language model (LLM) agents are widely deployed in real-world applications, where they leverage tools to retrieve and manipulate external data for complex tasks.<n>When interacting with untrusted data sources, tool responses may contain injected instructions that covertly influence agent behaviors and lead to malicious outcomes.<n>We propose a novel defensive task execution paradigm, called IPIGuard, to prevent malicious tool invocations at the source.
  arXiv  Detail & Related papers  (2025-08-21T07:08:16Z)
• BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks [58.959622170433725]
  BlindGuard is an unsupervised defense method that learns without requiring any attack-specific labels or prior knowledge of malicious behaviors.<n>We show that BlindGuard effectively detects diverse attack types (i.e., prompt injection, memory poisoning, and tool attack) across multi-agent systems.
  arXiv  Detail & Related papers  (2025-08-11T16:04:47Z)
• AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents [54.29555239363013]
  We propose a generic black-box fuzzing framework, AgentVigil, to automatically discover and exploit indirect prompt injection vulnerabilities.<n>We evaluate AgentVigil on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o.<n>We apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.
  arXiv  Detail & Related papers  (2025-05-09T07:40:17Z)
• AdvAgent: Controllable Blackbox Red-teaming on Web Agents [22.682464365220916]
  AdvAgent is a black-box red-teaming framework for attacking web agents.<n>It employs a reinforcement learning-based pipeline to train an adversarial prompter model.<n>With careful attack design, these prompts effectively exploit agent weaknesses while maintaining stealthiness and controllability.
  arXiv  Detail & Related papers  (2024-10-22T20:18:26Z)
• Dissecting Adversarial Robustness of Multimodal LM Agents [70.2077308846307]
  We manually create 200 targeted adversarial tasks and evaluation scripts in a realistic threat model on top of VisualWebArena.<n>We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search.<n>We also use ARE to rigorously evaluate how the robustness changes as new components are added.
  arXiv  Detail & Related papers  (2024-06-18T17:32:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.