FireClaw

You can mitigate prompt injection by routing web fetches through a defense-in-depth sanitization pipeline, applying DNS pre-checks, structural sanitization, isolated LLM summarization, and output scanning to return safe content to AI agents.

The best way to sanitize untrusted web data for LLM summarization is an isolated pipeline that strips malicious structures first, summarizes the content in a sandboxed environment, and scans the final output for injection payloads.

Domain trust tiering optimizes agent web fetching by classifying sources, allowing the sanitization pipeline to bypass heavy processing for trusted domains while enforcing strict structural sanitization and output scanning for untrusted ones.

Yes, you can audit log prompt injection attempts using JSONL audit logging and alerting features, which record pipeline events and trigger alerts to support incident response and bypass detection.

Canary tokens are needed in a prompt injection defense pipeline when you want to detect sanitization bypasses, verifying whether untrusted web content successfully injected malicious instructions into the isolated LLM summarization stage.

Yes, the sanitization pipeline works for agent workflows that perform memory lookups or issue tools via prompts, sanitizing external data before it reaches the agent's core reasoning logic.