ceLLMate: Sandboxing Browser AI Agents
Luoxi Meng, Henry Feng, Ilia Shumailov, Earlence Fernandes

TL;DR
ceLLMate is a browser extension framework that sandbox BUAs at the HTTP layer, effectively reducing prompt injection risks while maintaining acceptable performance overhead.
Contribution
It introduces a novel HTTP-layer sandboxing approach for browser AI agents, addressing security vulnerabilities in web interactions.
Findings
Successfully blocks prompt injection attacks in WASP benchmark
Achieves 7.25-15% latency overhead
Provides a general, agent-agnostic sandboxing solution
Abstract
Browser-using agents (BUAs) are an emerging class of AI agents that interact with web browsers in human-like ways, including clicking, scrolling, filling forms, and navigating across pages. While these agents help automate repetitive online tasks, they are vulnerable to prompt injection attacks that trick an agent into performing undesired actions, such as leaking private information or issuing unintended state-changing requests. We propose ceLLMate, a browser-level sandboxing framework that restricts the agent's ambient authority and reduces the blast radius of prompt injections. We address the semantic gap challenge that is fundamental to BUAs -- writing and enforcing security policies for low-level UI tools like clicks and keystrokes is brittle and error-prone. Our core insight is to perform sandboxing at the HTTP layer because all side-effecting UI operations will result in network…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWeb Application Security Vulnerabilities · Security and Verification in Computing · Spam and Phishing Detection
