Perplexity has introduced BrowseSafe, a new open-source detection model designed to secure the next generation of AI-powered browsers.
As AI assistants increasingly navigate the web to perform tasks rather than just answer questions, they face new threats from prompt injections, malicious instructions hidden in a webpage’s HTML designed to hijack the agent’s behavior.
BrowseSafe scans web pages in real-time to identify these threats without slowing down the browsing experience, ensuring that the assistant remains under the user’s control.
Alongside the detection model, Perplexity released BrowseSafe-Bench, a comprehensive evaluation suite containing over 14,000 real-world attack scenarios.
This benchmark helps developers test their defenses against sophisticated injection strategies, including attacks hidden in comments, footers, or multilingual text.
By open-sourcing these tools, Perplexity aims to help the broader developer community build safer autonomous agents that can navigate the untrusted web securely.
Key Takeaways:
- BrowseSafe is a real-time detection model designed to catch prompt injections in web pages.
- The release includes a benchmark with over 14,000 attack scenarios for testing.
- The tools are open-source to help developers harden AI agents against web-based threats.
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