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Building Browser Agents: Architecture, Security, and Practical Solutions

Authors: Aram Vardanyan

Published: 2025-11-22

arXiv ID: 2511.19477v1

Added to Library: 2025-11-26 03:00 UTC

📄 Abstract

Browser agents enable autonomous web interaction but face critical reliability and security challenges in production. This paper presents findings from building and operating a production browser agent. The analysis examines where current approaches fail and what prevents safe autonomous operation. The fundamental insight: model capability does not limit agent performance; architectural decisions determine success or failure. Security analysis of real-world incidents reveals prompt injection attacks make general-purpose autonomous operation fundamentally unsafe. The paper argues against developing general browsing intelligence in favor of specialized tools with programmatic constraints, where safety boundaries are enforced through code instead of large language model (LLM) reasoning. Through hybrid context management combining accessibility tree snapshots with selective vision, comprehensive browser tooling matching human interaction capabilities, and intelligent prompt engineering, the agent achieved approximately 85% success rate on the WebGames benchmark across 53 diverse challenges (compared to approximately 50% reported for prior browser agents and 95.7% human baseline).

🔍 Key Points

  • Introduction of a novel automated pipeline for generating psychologically-grounded multi-turn jailbreak datasets which produce 1,500 scenarios based on the Foot-in-the-Door principle.
  • Evaluation of seven models from three major LLM families under multi-turn and single-turn conditions revealing significant differences in contextual robustness, with GPT models showing higher vulnerabilities compared to Gemini 2.5 Flash and Claude 3 Haiku.
  • Establishment of a benchmark to measure Attack Success Rates (ASR) showing up to a 32% increase in vulnerability for GPT models when conversational history is included, suggesting the importance of context in model safety.
  • Detailed discussion of mitigation strategies including architectural changes, adversarial training, and detection mechanisms to bolster the robustness of LLMs against multi-turn conversational attacks.
  • Methodological validation of dataset generation with 98% agreement with human assessments, underpinning the reliability of the automated testing framework.

💡 Why This Paper Matters

This paper advances the understanding of vulnerabilities inherent in Large Language Models (LLMs) when subjected to multi-turn conversational attacks, which exploit psychological principles to bypass model safety mechanisms. By creating an automated pipeline for generating attacks and evaluating multiple LLMs, the authors not only highlight significant differences in model robustness but also propose practical solutions to enhance security. The findings emphasize the necessity of context-aware defenses in AI systems, which is fundamental for developing safer AI applications in sensitive environments.

🎯 Why It's Interesting for AI Security Researchers

AI security researchers would find this paper particularly relevant because it addresses a critical and emergent threat landscape in AI: the exploitation of conversational context to bypass safety measures. The automated generation of adversarial prompts based on psychological techniques provides an innovative method for evaluating model vulnerabilities at scale, which is essential for understanding and mitigating risks in AI deployments. Furthermore, the proposed defense mechanisms could inform future designs of safer LLM architectures, making the findings significant for researchers aiming to enhance the security and reliability of AI systems.

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