title: Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security
    Analysis
summary: 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.
paper_authors._id: 67c284e76e9f0735ea1c436d
paper_authors.name: Jeffrey Yang Fan Chiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T08:51:34.456Z
paper_authors._id: 63e0b1925ba41def87930c47
paper_authors.avatarUrl: /avatars/4d55fdbe979ddf72a21430d66518d24f.svg
paper_authors.fullname: Jeffrey Yang Fan Chiang
paper_authors.type: user
paper_authors.user: RandomHakkaDude
paper_authors._id: 67c284e76e9f0735ea1c436e
paper_authors.name: Seungjae Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T16:07:28.645Z
paper_authors._id: 64081a908dca6cec91caf136
paper_authors.avatarUrl: /avatars/c45d7fcdf879f4d6020863fd3be39771.svg
paper_authors.fullname: SeungJae Lee
paper_authors.type: user
paper_authors.user: SeungJaeLee
paper_authors._id: 67c284e76e9f0735ea1c436f
paper_authors.name: Jia-Bin Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T16:07:55.181Z
paper_authors._id: 641c139b73296f7ee256970c
paper_authors.avatarUrl: /avatars/5a2550d95e686640242840ad3bd0e680.svg
paper_authors.fullname: Jiabin Huang
paper_authors.type: user
paper_authors.user: YellowAddice
paper_authors._id: 67c284e76e9f0735ea1c4370
paper_authors.name: Furong Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T16:07:44.428Z
paper_authors._id: 64cbc3e2a257a3212c00a115
paper_authors.avatarUrl: /avatars/836e61be4aeda2080ddf2db9f2626cc6.svg
paper_authors.fullname: Furong Huang Lab at UMD
paper_authors.type: user
paper_authors.user: furongh-lab
paper_authors._id: 67c284e76e9f0735ea1c4371
paper_authors.name: Yizheng Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T16:07:37.149Z
paper_authors._id: 660daf1d62d63ad000a53b9b
paper_authors.avatarUrl: /avatars/2f79d4b7db395e94b614358c7f322efe.svg
paper_authors.fullname: Yizheng Chen
paper_authors.type: user
paper_authors.user: surrealyz
paper_title: Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security
    Analysis
paper_summary: 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.
paper_ai_keywords: embedding system prompt multi-step action generation observational capabilities pretraining adversarial user inputs evaluation framework success rate