Building safer AI browsers with BrowseSafe

BrowseSafe is an open-source, real-time detection model and accompanying BrowseSafe-Bench benchmark designed to protect browser-based AI agents from prompt injection attacks embedded in untrusted web content. As agents increasingly read and act on entire pages, malicious instructions can be concealed in comments, templates, invisible HTML attributes, form fields, footers, or ordinary-looking multilingual text to redirect an agent from a user’s intended task. BrowseSafe scans page HTML before an agent processes it, offering a faster and less costly alternative to applying general-purpose language models to every page, while BrowseSafe-Bench provides 14,719 realistic malicious and benign examples spanning 11 attack types, nine placement strategies, and three language styles. Evaluation indicates that explicit attacks are generally easier to identify, whereas indirect, multilingual, and visibly placed injections are more difficult, exposing limitations in detectors that associate threats mainly with hidden content. The model is intended as one component of a defense-in-depth system that also limits tool permissions, treats web-derived data as untrusted, and requires user confirmation for sensitive actions.

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