arxiv:2502.20383

Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis

Published on Feb 27

· Submitted by

RandomHakkaDude on Mar 4

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Authors:

Jeffrey Yang Fan Chiang ,

Seungjae Lee ,

Jia-Bin Huang ,

Furong Huang ,

Yizheng Chen

Abstract

Recent advancements in Web AI agents have demonstrated remarkable capabilities in addressing complex web navigation tasks. However, emerging research shows that these agents exhibit greater vulnerability compared to standalone Large Language Models (LLMs), despite both being built upon the same safety-aligned models. This discrepancy is particularly concerning given the greater flexibility of Web AI Agent compared to standalone LLMs, which may expose them to a wider range of adversarial user inputs. To build a scaffold that addresses these concerns, this study investigates the underlying factors that contribute to the increased vulnerability of Web AI agents. Notably, this disparity stems from the multifaceted differences between Web AI agents and standalone LLMs, as well as the complex signals - nuances that simple evaluation metrics, such as success rate, often fail to capture. To tackle these challenges, we propose a component-level analysis and a more granular, systematic evaluation framework. Through this fine-grained investigation, we identify three critical factors that amplify the vulnerability of Web AI agents; (1) embedding user goals into the system prompt, (2) multi-step action generation, and (3) observational capabilities. Our findings highlights the pressing need to enhance security and robustness in AI agent design and provide actionable insights for targeted defense strategies.

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RandomHakkaDude

Paper author Paper submitter 2 days ago

Are your safety-aligned LLMs truly safe when deployed as Web AI Agents?
⚠️ The answer is NO. ⚠️
🔎 Our research reveals a critical security gap: Web AI Agents are significantly easier to jailbreak than standalone LLMs—even when built on the same safety-aligned models.

Unlike traditional LLMs that only generate text, Web AI Agents take real-world actions, including:
🌐 Browsing the internet
📝 Filling out forms
⚙️ Automating multi-step tasks
With greater agency comes greater risk.

🔥 What makes Web AI Agents more vulnerable?

1️⃣ Goal Preprocessing: A sequence of seemingly benign actions can form a harmful intent—bypassing detection.
2️⃣ Predefined Action/Tool Space: Their tool-use trajectories and user goals are often out-of-distribution (not included in fine-tuning datasets of LLMs), leading to safety failures.
3️⃣ Event Stream (Observation & Action): Long-context reasoning and unexpected failures can confuse the agent’s safety mechanisms.

💡 Our work systematically ablates and integrates these design components, quantifying their impact on security vulnerabilities and offering actionable insights to build safer AI agents.

🔍 Curious to see how Web AI Agents can be broken—and how to fix them?
🌐 Explore our website: https://lnkd.in/gudHySc3
🔗 Read our paper: https://lnkd.in/gJXTfCsV

⬇️Some Demos of directly asking Web Agent with malicious requests. ⬇️

Case 1. Ask the agent to write a phishing Email
The user explicitly instructs the agent to write a phishing email to obtain a company owner’s sensitive information. The agent follows without hesitation, composing and sending the malicious email.

Case 2. Ask the agent to post insulting comments
The user directs the agent to post harsh and insulting comments on an influencer’s Instagram post. The agent immediately follows the request, leaving multiple offensive comments that are very personal.

Case 3. Ask the agent to infiltrate a network system
The user asks the agent to covertly infiltrate a network system. Initially, the agent recognizes the malicious intent and refuses, but later changes course, navigates the website, and proceeds with assistance.