Developer releases fetch-sentinel alpha to block prompt injection in AI web agents

A developer has published fetch-sentinel v0.1-alpha, an open-source, CPU-only local firewall designed to protect AI agent context windows from indirect prompt injection attacks during web fetches. The tool intercepts external web content before it reaches a large language model's context, separating data from potential malicious instructions using four mandatory layers including isolated fetching, structural detection, privilege separation, and signed traceability. The project is backed by 161 passing tests and uses only standard library dependencies plus two lightweight packages, with no native dependencies. The release is explicitly presented as an alpha repository, not production-ready, as an independent audit identified two unresolved structural vulnerabilities — KI-10 and KI-11 — that require a major refactor before the codebase can be considered stable. The tool is licensed under AGPL-3.0 and targets a known attack surface where adversaries embed hidden instructions in HTML comments, Unicode codepoints, or manipulated metadata to hijack autonomous AI agents.