# From Monoliths to Swarms: A Study of Attack Surface Evolution in the Transition to Multi-Agent Web Systems

arXiv: https://arxiv.org/abs/2608.00202 · HTML: https://mlsanity.com/paper/2608.00202/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 35 of its 48 references that are in the map.
- First posted on arXiv 31 Jul 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 30 of 35)

- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [GPT-4V(ision) is a Generalist Web Agent, if Grounded](https://mlsanity.com/paper/2401.01614/) (2024)
- [Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents](https://mlsanity.com/paper/2410.02644/) (2024)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?](https://mlsanity.com/paper/2403.07718/) (2024)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks](https://mlsanity.com/paper/2411.04468/) (2024)
- [The BrowserGym Ecosystem for Web Agent Research](https://mlsanity.com/paper/2412.05467/) (2024)
- [AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?](https://mlsanity.com/paper/2407.15711/) (2024)
- [ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents](https://mlsanity.com/paper/2410.06703/) (2024)
- [Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems](https://mlsanity.com/paper/2410.07283/) (2024)
- [SafeArena: Evaluating the Safety of Autonomous Web Agents](https://mlsanity.com/paper/2503.04957/) (2025)
- [AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents](https://mlsanity.com/paper/2503.09780/) (2025)
- [Trading Inference-Time Compute for Adversarial Robustness](https://mlsanity.com/paper/2501.18841/) (2025)
- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)
- [Multi-Agent Systems Execute Arbitrary Malicious Code](https://mlsanity.com/paper/2503.12188/) (2025)
- [textitAgents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks](https://mlsanity.com/paper/2504.00218/) (2025)
- [MAGPIE: A benchmark for Multi-AGent contextual PrIvacy Evaluation](https://mlsanity.com/paper/2510.15186/) (2025)
- [Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis](https://mlsanity.com/paper/2502.20383/) (2025)
- [TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems](https://mlsanity.com/paper/2511.05269/) (2025)
- [WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents](https://mlsanity.com/paper/2510.01354/) (2025)
- [MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks](https://mlsanity.com/paper/2602.09222/) (2026)
- [ConVerse: Benchmarking Contextual Safety in Agent-to-Agent Conversations](https://mlsanity.com/paper/2511.05359/) (2025)
- [Manipulating LLM Web Agents with Indirect Prompt Injection Attack via HTML Accessibility Tree](https://mlsanity.com/paper/2507.14799/) (2025)
- [Breaking and Fixing Defenses Against Control-Flow Hijacking in Multi-Agent Systems](https://mlsanity.com/paper/2510.17276/) (2025)
- [The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerability](https://mlsanity.com/paper/2510.18563/) (2025)

## Abstract (arXiv)

Large Language Model (LLM)-based web agents are increasingly evolving from single-agent systems (SAS) to multi-agent systems (MAS). While MAS can lead to improved task performance by decomposing complex tasks across specialized sub-agents, such role decomposition introduces new structural attack surfaces that are absent in SAS. This expanded attack surface remains poorly understood and inadequately categorized. To address this, we propose a taxonomy to categorize attack vectors specific to web-based MAS, accounting for vulnerabilities introduced or amplified by the involvement of multiple agents. We further present a test-bed WebMASLab to analyze web agent security against a fully external, web-only adversary. To isolate the effect of architecture, we keep the user task, tool surface, and browser substrate fixed, and compare single- and multi-agent setups. We evaluate three adversarial scenarios, across three conditions (baseline, prompt-hardened, and reasoning-enabled), including a novel MAS-specific Telephone Loop attack that exploits cross-agent delegation to create cyclical task loops. The attack is inert against SAS but compromises MAS when powered by three of the four frontier models evaluated (Claude Sonnet 4.5, GPT-5.2, GPT-5.4), averaging 80% across them at baseline. Only the fourth model, Claude Sonnet 4.6, resists the attack with a 92% detection rate. For the rest, the detection is 0% at baseline, reaching 33% with prompt-hardening for one model. We also show that obvious defenses do not generalize; prompt-hardening collapses one model's ASR from 100% to 8% while providing only modest reduction to the others. Our findings demonstrate that the transition from single- to multi-agent web systems changes the security landscape. Role specialization may not only lead to performance optimization but also introduce new architectural risks that require further study and defenses.