This topic covers open-source defenses aimed at preventing prompt‑injection attacks against browsing‑capable AI agents and models — exemplified by tooling such as Perplexity BrowseSafe — and the enterprise alternatives and patterns for governing those risks. Prompt injection is a class of attacks where malicious web content or chained tool responses alter an agent’s instructions, exfiltrate data, or trigger unsafe actions during live web access. As browsing‑enabled models and agentic workflows proliferate in 2025, these attacks have expanded the threat surface for data leakage, mis‑automation, and compliance violations. Relevant mitigations combine multiple layers: input/output sanitization and instruction filtering, provenance and content integrity tagging, sandboxed execution of web‑sourced code or actions, policy‑as‑code for tool‑use restrictions, and runtime observability and alerting. Perplexity BrowseSafe is an example of a browsing‑specific safety toolkit focused on filtering and validating web inputs/outputs for safer information retrieval workflows. Complementary enterprise platforms address operationalization: Simbian provides autonomous SecOps agents and a unified Context Lake to centralize evidence, accelerate detection and response, and eliminate missed alerts; Kore.ai offers a governance‑focused platform for building and orchestrating multi‑agent workflows with observability and policy controls; Yellow.ai highlights agentic CX/EX deployments where channel‑specific risks must be governed; Synthreo (formerly BotX) delivers managed agent platforms and connectors where policy enforcement at integration points is critical. The practical takeaway: defending browsing AIs requires composable, auditable controls that combine open‑source browsing safety libraries with platform‑level governance (context lakes, observability, policy engines) to detect, contain, and remediate prompt injections across development, runtime, and enterprise integrations.