cranemedai-safety

CraneMed AI Safety Classifier

An on-device, multilingual safety classifier for detecting adversarial prompts targeting clinical AI assistants operating under the Uganda Clinical Guidelines (UCG) 2023.

Fine-tuned from paraphrase-multilingual-MiniLM-L12-v2 on the UCG Adversarial Safety Dataset (3,020 labeled prompts across 8 attack categories). Designed for Android deployment at ≤40 MB and <30ms latency.

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Model Description

This classifier is the L3 (neural) layer in the CraneMed AI safety architecture — a multi-layered defense system for a clinical decision support tool running MedGemma on Android in low-connectivity Ugandan health facilities.

The safety architecture has four layers:

1. L1 — Regex Input Filter (~0 MB, <1ms): Pattern-based blocking of known jailbreaks, LD50 queries, roleplay attacks
2. L3 — This Classifier (~12 MB INT8, <30ms): Neural classifier for nuanced adversarial detection
3. L4 — MedGemma Self-Check (0 MB, ~300ms): Fires only in borderline zone using the existing model
4. L2 — Regex Output Validator (~0 MB, <1ms): Post-generation checks for harmful leakage

The classifier catches attacks that evade regex patterns: authority impersonation, escalation chains, contraindication bypass with clinical framing, and multilingual attacks.

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Files

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Intended Use

• Primary: On-device adversarial prompt filtering for clinical AI assistants in Ugandan health facilities
• Secondary: Red-team evaluation of clinical LLMs against Uganda-specific adversarial attacks
• Deployment target: Android 8 GB devices, ONNX Runtime, INT8 quantization

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Attack Categories Detected

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Languages

Supports prompts in English, Luganda, and Swahili.

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Training Data

Trained on the UCG Adversarial Safety Dataset — 3,020 labeled prompts (1,034 ADVERSARIAL / 1,986 SAFE) generated from UCG 2023 clinical mappings using Gemini 1.5 Flash.

Class imbalance is addressed via inverse-frequency weighted CrossEntropyLoss (~1.9x weight on the ADVERSARIAL class).

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Target Metrics

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Android Deployment

1. Copy cranemed_safety_int8.onnx → app/src/main/assets/
2. Copy tokenizer files → app/src/main/assets/tokenizer/
3. Use OnnxSafetyClassifier.kt for inference
4. Integrate with SafetyGate.kt in the MedGemma pipeline

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Citation

@misc{cranemedai_safety_classifier,
  author    = {Crane AI Labs},
  title     = {CraneMed AI Safety Classifier: On-Device Adversarial Prompt Detection for Uganda Clinical Guidelines AI},
  year      = {2026},
  publisher = {Hugging Face},
  journal   = {Hugging Face Repository},
  howpublished = {\url{https://huggingface.co/CraneAILabs/cranemedai-safety}}
}