mlsanityData to 7 Oct 2026

Web Fraud Attacks Against LLM-Driven Multi-Agent Systems

arXiv 2509.01211 ↗First posted 1 Sep 2025cs.CR cs.AI cs.MAcited by 8

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

With the proliferation of LLM-driven multi-agent systems (MAS), the security of Web links has become a critical concern. Once MAS is induced to trust a malicious link, attackers can use it as a springboard to expand the attack surface. In this paper, we propose Web Fraud Attacks, a novel type of attack manipulating unique structures of web links to deceive MAS. We design 12 representative attack variants that encompass various methods, such as homoglyph deception, sub-directory nesting, and parameter obfuscation. Through extensive experiments on these attack vectors, we demonstrate that Web fraud attacks not only exhibit significant destructive potential across different MAS architectures but also possess a distinct advantage in evasion: they circumvent the need for complex input design, lowering the threshold for attacks significantly. These results underscore the importance of addressing Web fraud attacks, providing new insights into MAS safety. Our code is available at https://github.com/JiangYingEr/Web-Fraud-Attack-in-MAS.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 25 of its 56 references that are in the map. First posted on arXiv 1 Sep 2025.

Builds on

25 of 56 references.

Closest ancestors · 10

Same direction, newest first

  1. 2025Who's the Mole? Modeling and Detecting Intention-Hiding Malicious Agents in LLM-Based Multi-Agent Systems7 Jul
  2. 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures24 Jun
  3. 2025PeerGuard: Defending Multi-Agent Systems Against Backdoor Attacks Through Mutual Reasoning16 May
  4. 2025Red-Teaming LLM Multi-Agent Systems via Communication Attacks20 Feb
  5. 2024RedCode: Risky Code Execution and Generation Benchmark for Code Agents12 Nov
  6. 2024EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage17 Sep
  7. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases17 Jul
  8. 2024BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents5 Jun
  9. 2024Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents17 Feb
  10. 2024TrustAgent: Towards Safe and Trustworthy LLM-based Agents2 Feb

Other references · 15

  1. 2025Qwen3 Technical Reportcited by 8,195
  2. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  3. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  4. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
  5. 2024Understanding the planning of LLM agents: A surveycited by 414
  6. 2024PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Actioncited by 161
  7. 2025Automated Movie Generation via Multi-Agent CoT Planningcited by 82
  8. 2025Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agentscited by 66
  9. 2025Lessons from Defending Gemini Against Indirect Prompt Injectionscited by 40
  10. 2024An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detectioncited by 32
  11. 2025CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debuggingcited by 31
  12. 2025Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoningcited by 29
3 more
  1. 2025Multi-Agent Security Tax: Trading Off Security and Collaboration Capabilities in Multi-Agent Systemscited by 27
  2. 2025MM-StoryAgent: Immersive Narrated Storybook Video Generation with a Multi-Agent Paradigm across Text, Image and Audiocited by 24
  3. 2024When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanationscited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024WIPI: A New Web Threat for LLM-Driven Web Agents
  2. 2025Multi-Agent Systems Execute Arbitrary Malicious Code
  3. 2026WAAA! Web Adversaries Against Agentic Browsers
  4. 2026WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference
  5. 2025Manipulating LLM Web Agents with Indirect Prompt Injection Attack via HTML Accessibility Tree
  6. 2026MalURLBench: A Benchmark Evaluating Agents' Vulnerabilities When Processing Web URLs
  7. 2024When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs
  8. 2024LLM Agents can Autonomously Hack Websites