GitHub Issue: AI Security Pipeline Hardened with Pre-Screening & Retroactive Redaction to Close Critical Context Window Vulnerability

A critical security vulnerability in an AI agent's pipeline has been addressed, where malicious prompts blocked by security systems could persist in the Large Language Model's (LLM) context window. The fix fundamentally alters the flow of user interaction, implementing a mandatory pre-screening gate for all prompts before they reach the LLM. This fail-closed system ensures that if the security pipeline is unreachable, the default action is to block the prompt, a reversal from a previous, riskier default of allowing it through.

The new architecture introduces a tiered response system based on policy actions. Prompts can be ALLOWED, SANITIZED with a low-risk warning, flagged as REQUIRING_APPROVAL with a heavy warning, or outright BLOCKED/QUARANTINED. In the latter case, the prompt is redacted to a placeholder, and the LLM is never invoked. Crucially, the fix also includes retroactive redaction: if a pre-screened prompt is allowed but a subsequent tool call is later blocked by the security gateway, the original user message is scrubbed from the conversation history, preventing residual influence.

Beyond the core vulnerability, the changes include significant hardening of the system's operational integrity. This involves handling malformed LLM tool arguments, broadening exception handling for network calls (httpx), fixing issues with conversation state during maximum iteration limits, validating configuration, and adding warnings for missing API keys. The update, which closes GitHub issue #13, represents a substantial shift towards a more defensible and auditable security posture for the AI agent, moving from a reactive to a proactive and persistent content filtering model.