Hey HN! I'm launching InferShield, an open source security proxy that sits between your application and LLM providers (OpenAI, Anthropic, etc.) to detect and block threats in real-time.

I've been talking to CISOs at banks and hospitals who are deploying LLMs without proper security. Their existing tools can't handle LLM-specific threats:

- Prompt injection (attackers manipulate LLM behavior, WAFs can't detect it) - Data exfiltration (sensitive data leaks through LLM responses) - Jailbreak attempts (users bypass safety guardrails using encoding)

Gartner predicts 60% of AI enterprises will face a security incident by 2027.

InferShield is a drop-in proxy with real-time threat detection, multi-encoding detection (Base64, hex, URL, Unicode), complete audit logs, and risk scoring. Self-hosted, provider-agnostic, zero code changes required.

v0.1 MVP is live today with 95%+ detection rate (red team tested). MIT licensed, free forever. Enterprise tier coming for compliance features.

Website: https://infershield.io Quick start: docker pull infershield/proxy:latest

Feedback welcome! What LLM security challenges are you facing?