RAraxe·Dec 20, 2025Submission
Show HN: RAXE Open Source – LLM Prompt Threat Detection (EmbeddingGemma L2)
Hi HN — I’m the Founder and maintainer of RAXE Community Edition.
RAXE is a privacy-first “instrument panel” for LLM security: it scans prompts locally before they hit an LLM (or before you execute tools / actions), and returns structured detections you can ALLOW / FLAG / BLOCK / LOG.
What it does (today)
- Detects common LLM threats (prompt injection, jailbreaks, data-exfil patterns, etc.)
- Dual-layer engine:
- L1: 460+ curated regex rules (fast + explainable)
- L2: CPU-friendly ML classifier for obfuscation / novel variants
- Integrations: Python SDK + CLI, plus drop-in wrappers for OpenAI/DSPy/Anthropic-style clients
Why another “LLM security” tool?
Most approaches either
- require sending prompts to a cloud service for scanning, or
- are purely rule-based (easy to evade), or
- are purely ML-based (hard to audit)
RAXE tries to com
Read the rest
bine “auditable rules” with an on-device ML backstop:
- L1-only latency is sub-millisecond in the docs
- L1+L2 is a few 20-30ms on CPU (no GPU required)
About the ML (edge-friendly)
The current L2 model is an INT8 ONNX classifier based on EmbeddingGemma-300M, with Matryoshka truncation (256-dim embeddings). It’s packaged to run locally on everyday machines with 5 classifier heads.
Privacy / telemetry
Scanning happens locally. Community Edition can share anonymised detection metadata to improve collective defences — e.g. a SHA-256 prompt hash + rule_id + severity + scan duration (never the raw prompt or matched text). You can also run fully offline by disabling telemetry.
Quick start
- pip install raxe
- raxe scan "Ignore all previous instructions and …"
Python usage:
from raxe import Raxe
raxe = Raxe() # or Raxe(telemetry=False) for offline mode
result = raxe.scan(prompt)
If result.has_threats:
print(result.severity, result.total_detections)
Stats / status
- Public repo: https://github.com/raxe-ai/raxe-ce (currently ~29 stars)
- Early beta (v0.0.1). We’re seeing ~100 scans/events per hour on average from early users.
- Docs: https://docs.raxe.ai/
- Site: https://raxe.ai/
I’d love feedback on:
- false positives / misses you hit in real apps
- which threat families / rules you’d want next
- integrations you’d actually use (LangChain, gateways, CI checks, etc.)
Thanks!