OnGarde – Runtime content security proxy for self-hosted AI agents

Name: OnGarde – Runtime content security proxy for self-hosted AI agents
Availability: InStock
Author: antimaterial

by antimaterial·Feb 26, 2026·2 points·1 comment

Visit Project View on HN

AI Analysis

●●●BangerSolve My ProblemSlick

Transparent proxy blocks agent leaks to LLM APIs without touching agent code.

Strengths

•Zero code changes required: swap baseUrl, instant coverage for any OpenAI-compatible agent
•Fail-safe architecture: blocks on error/timeout, not silent pass-through; 1,222 passing tests
•PII, secret, injection, shell command detection with spaCy NER and pattern matching

Weaknesses

•Detection heuristics not formally verified (unlike SkillFortify); false positives could break workflows
•Relies on spaCy NER for PII, which has known gaps on structured formats (credit cards, SSNs)

Post Description

Built this because I had heard some horror stories about companies leaking PII from high compliance environments to ChatGPT. I wanted something that would auto-filter any dangerous traffic between my AI agent and the LLM API without requiring code changes in the agent itself.

The filtering list has expanded a bit to include PII, secret keys and I've started a prompt injection library thats being filtered on as well.

The problem: self-hosted agent platforms (OpenClaw, Agent Zero, CrewAI) have no runtime content layer. If your agent leaks an API key, gets prompt injected, or decides to forward someone's SSN to GPT-4, nothing stops it. The platforms don't try to stop it either.

OnGarde is a proxy. You change one line in your config (swap baseUrl) and every request gets scanned before it leaves. Catches credentials, PII, prompt injection, dangerous shell commands. If the scanner fails, it blocks it; never silently passes through.

npx @ongarde/openclaw init handles the OpenClaw setup automatically. Also on PyPI if you're doing something custom.

Dashboard is localhost-only with a SQLite audit log. Nothing phones home.

v1 just shipped: https://github.com/AntimatterEnterprises/ongarde/releases/ta...

I am looking for feedback on this project. Let me hear your thoughts.