AdvAgent: Controllable Blackbox Red-teaming on Web Agents

• URL: http://arxiv.org/abs/2410.17401v3
• Date: Wed, 28 May 2025 15:38:51 GMT
• Title: AdvAgent: Controllable Blackbox Red-teaming on Web Agents
• Authors: Chejian Xu, Mintong Kang, Jiawei Zhang, Zeyi Liao, Lingbo Mo, Mengqi Yuan, Huan Sun, Bo Li,
• Abstract summary: 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.
• Score: 22.682464365220916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation model-based agents are increasingly used to automate complex tasks, enhancing efficiency and productivity. However, their access to sensitive resources and autonomous decision-making also introduce significant security risks, where successful attacks could lead to severe consequences. To systematically uncover these vulnerabilities, we propose AdvAgent, a black-box red-teaming framework for attacking web agents. Unlike existing approaches, AdvAgent employs a reinforcement learning-based pipeline to train an adversarial prompter model that optimizes adversarial prompts using feedback from the black-box agent. With careful attack design, these prompts effectively exploit agent weaknesses while maintaining stealthiness and controllability. Extensive evaluations demonstrate that AdvAgent achieves high success rates against state-of-the-art GPT-4-based web agents across diverse web tasks. Furthermore, we find that existing prompt-based defenses provide only limited protection, leaving agents vulnerable to our framework. These findings highlight critical vulnerabilities in current web agents and emphasize the urgent need for stronger defense mechanisms. We release code at https://ai-secure.github.io/AdvAgent/.

Related papers

• Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
  We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
  arXiv  Detail & Related papers  (2025-07-28T05:13:04Z)
• Towards Unifying Quantitative Security Benchmarking for Multi Agent Systems [0.0]
  Evolving AI systems increasingly deploy multi-agent architectures where autonomous agents collaborate, share information, and delegate tasks through developing protocols.<n>One such risk is a cascading risk: a breach in one agent can cascade through the system, compromising others by exploiting inter-agent trust.<n>In an ACI attack, a malicious input or tool exploit injected at one agent leads to cascading compromises and amplified downstream effects across agents that trust its outputs.
  arXiv  Detail & Related papers  (2025-07-23T13:51:28Z)
• Context manipulation attacks : Web agents are susceptible to corrupted memory [37.66661108936654]
  "Plan injection" is a novel context manipulation attack that corrupts these agents' internal task representations by targeting this vulnerable context.<n>We show that plan injections bypass robust prompt injection defenses, achieving up to 3x higher attack success rates than comparable prompt-based attacks.<n>Our findings highlight that secure memory handling must be a first-class concern in agentic systems.
  arXiv  Detail & Related papers  (2025-06-18T14:29:02Z)
• Your Agent Can Defend Itself against Backdoor Attacks [0.0]
  Large language model (LLM)-powered agents face significant security risks from backdoor attacks during training and fine-tuning.<n>We present ReAgent, a novel defense against a range of backdoor attacks on LLM-based agents.
  arXiv  Detail & Related papers  (2025-06-10T01:45:56Z)
• WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback [74.82886755416949]
  We identify key reasoning skills essential for effective web agents.<n>We reconstruct the agent's reasoning algorithms into chain-of-thought rationales.<n>Our approach yields significant improvements across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T14:03:37Z)
• The Hidden Dangers of Browsing AI Agents [0.0]
  This paper presents a comprehensive security evaluation of such agents, focusing on systemic vulnerabilities across multiple architectural layers.<n>Our work outlines the first end-to-end threat model for browsing agents and provides actionable guidance for securing their deployment in real-world environments.
  arXiv  Detail & Related papers  (2025-05-19T13:10:29Z)
• 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)
• WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks [36.97842000562324]
  A benchmark called WASP introduces realistic web agent hijacking objectives and an isolated environment to test them. Our evaluation shows that even AI agents backed by models with advanced reasoning capabilities are susceptible to low-effort human-written prompt injections. Agents begin executing the adversarial instruction between 16 and 86% of the time but only achieve the goal between 0 and 17% of the time.
  arXiv  Detail & Related papers  (2025-04-22T17:51:03Z)
• Manipulating Multimodal Agents via Cross-Modal Prompt Injection [34.35145839873915]
  We identify a critical yet previously overlooked security vulnerability in multimodal agents. We propose CrossInject, a novel attack framework in which attackers embed adversarial perturbations across multiple modalities. Our method outperforms existing injection attacks, achieving at least a +26.4% increase in attack success rates.
  arXiv  Detail & Related papers  (2025-04-19T16:28:03Z)
• DoomArena: A framework for Testing AI Agents Against Evolving Security Threats [84.94654617852322]
  We present DoomArena, a security evaluation framework for AI agents. It is a plug-in framework and integrates easily into realistic agentic frameworks. It is modular and decouples the development of attacks from details of the environment in which the agent is deployed.
  arXiv  Detail & Related papers  (2025-04-18T20:36:10Z)
• Tit-for-Tat: Safeguarding Large Vision-Language Models Against Jailbreak Attacks via Adversarial Defense [90.71884758066042]
  Large vision-language models (LVLMs) introduce a unique vulnerability: susceptibility to malicious attacks via visual inputs. We propose ESIII (Embedding Security Instructions Into Images), a novel methodology for transforming the visual space from a source of vulnerability into an active defense mechanism.
  arXiv  Detail & Related papers  (2025-03-14T17:39:45Z)
• Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks [88.84977282952602]
  A high volume of recent ML security literature focuses on attacks against aligned large language models (LLMs) In this paper, we analyze security and privacy vulnerabilities that are unique to LLM agents. We conduct a series of illustrative attacks on popular open-source and commercial agents, demonstrating the immediate practical implications of their vulnerabilities.
  arXiv  Detail & Related papers  (2025-02-12T17:19:36Z)
• MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents [60.30753230776882]
  LLM agents are vulnerable to indirect prompt injection (IPI) attacks, where malicious tasks embedded in tool-retrieved information can redirect the agent to take unauthorized actions.<n>We present MELON, a novel IPI defense that detects attacks by re-executing the agent's trajectory with a masked user prompt modified through a masking function.
  arXiv  Detail & Related papers  (2025-02-07T18:57:49Z)
• Attacking Vision-Language Computer Agents via Pop-ups [61.744008541021124]
  We show that VLM agents can be easily attacked by a set of carefully designed adversarial pop-ups. This distraction leads agents to click these pop-ups instead of performing the tasks as usual.
  arXiv  Detail & Related papers  (2024-11-04T18:56:42Z)
• The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks [2.6528263069045126]
  Large language models (LLMs) could soon become integral to autonomous cyber agents. We introduce novel defense strategies that exploit the inherent vulnerabilities of attacking LLMs. Our results show defense success rates of up to 90%, demonstrating the effectiveness of turning LLM vulnerabilities into defensive strategies.
  arXiv  Detail & Related papers  (2024-10-20T14:07:24Z)
• EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage [40.82238259404402]
  We conduct the first study on the privacy risks of generalist web agents in adversarial environments. First, we present a realistic threat model for attacks on the website, where we consider two adversarial targets: stealing users' specific PII or the entire user request. We collect 177 action steps that involve diverse PII categories on realistic websites from the Mind2Web, and conduct experiments using one of the most capable generalist web agent frameworks to date.
  arXiv  Detail & Related papers  (2024-09-17T15:49:44Z)
• AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases [73.04652687616286]
  We propose AgentPoison, the first backdoor attack targeting generic and RAG-based LLM agents by poisoning their long-term memory or RAG knowledge base. Unlike conventional backdoor attacks, AgentPoison requires no additional model training or fine-tuning. On each agent, AgentPoison achieves an average attack success rate higher than 80% with minimal impact on benign performance.
  arXiv  Detail & Related papers  (2024-07-17T17:59:47Z)
• 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. We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search. 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)
• Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees [16.16333915007336]
  Malicious URLs provide adversarial opportunities across various industries, including transportation, healthcare, energy, and banking. backdoor attacks involve manipulating a small percentage of training data labels, such as Label Flipping (LF), which changes benign labels to malicious ones and vice versa. We propose an innovative alarm system that detects the presence of poisoned labels and a defense mechanism designed to uncover the original class labels.
  arXiv  Detail & Related papers  (2024-03-05T14:21:57Z)
• WIPI: A New Web Threat for LLM-Driven Web Agents [28.651763099760664]
  We introduce a novel threat, WIPI, that indirectly controls Web Agent to execute malicious instructions embedded in publicly accessible webpages. To launch a successful WIPI works in a black-box environment. Our methodology achieves an average attack success rate (ASR) exceeding 90% even in pure black-box scenarios.
  arXiv  Detail & Related papers  (2024-02-26T19:01:54Z)
• SENet: Visual Detection of Online Social Engineering Attack Campaigns [3.858859576352153]
  Social engineering (SE) aims at deceiving users into performing actions that may compromise their security and privacy. SEShield is a framework for in-browser detection of social engineering attacks.
  arXiv  Detail & Related papers  (2024-01-10T22:25:44Z)
• Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
  We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
  arXiv  Detail & Related papers  (2023-12-20T05:06:01Z)
• BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning [85.2564206440109]
  This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses. We introduce the emphtoolns attack, which is resistant to backdoor detection and model fine-tuning defenses.
  arXiv  Detail & Related papers  (2023-11-20T02:21:49Z)
• Baseline Defenses for Adversarial Attacks Against Aligned Language Models [109.75753454188705]
  Recent work shows that text moderations can produce jailbreaking prompts that bypass defenses. We look at three types of defenses: detection (perplexity based), input preprocessing (paraphrase and retokenization), and adversarial training. We find that the weakness of existing discretes for text, combined with the relatively high costs of optimization, makes standard adaptive attacks more challenging for LLMs.
  arXiv  Detail & Related papers  (2023-09-01T17:59:44Z)
• On the Effectiveness of Adversarial Training against Backdoor Attacks [111.8963365326168]
  A backdoored model always predicts a target class in the presence of a predefined trigger pattern. In general, adversarial training is believed to defend against backdoor attacks. We propose a hybrid strategy which provides satisfactory robustness across different backdoor attacks.
  arXiv  Detail & Related papers  (2022-02-22T02:24:46Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)

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.