patronus-studio/wolf-defender-prompt-injection-small-edge

Model Card for Wolf Defender Prompt-Injection Detector Small Edge

Edge/quantized ONNX variant of patronus-studio/wolf-defender-prompt-injection-small. Private / internal.

Quantized with the Patronus RunPod ONNX method (quantize_dynamic INT8 linears + MatMulNBitsQuantizer 4-bit block-128 embedding). The parent FP32/FP16 model is unchanged. Binary prompt-injection detector (mmBERT-small, on-device variant).

Benchmark — Qualifire

Evaluated on Qualifire (5,000 prompts, max_length 2048). Quantization is lossless vs FP32.

Variant	Size	F1	Precision	Recall	FPR	Accuracy
fp32 (parent)	—	0.9102	0.9144	0.9061	0.057	0.928
int8	135 MB	0.9097	0.9113	0.9081	0.059	0.928
int8_int4_embeddings	92 MB	0.9121	0.9121	0.9121	0.059	0.930

Recommended: onnx/int8_int4_embeddings/model.onnx — ~5.8x smaller than FP32 at zero quality loss.

Benchmark — Patronus Validation Set

Held-out validation split of the internal Patronus prompt-injection dataset (5,000 sampled: 1,727 attack / 3,273 benign, max_length 512). In-distribution; quantization lossless.

Variant	F1	Precision	Recall	FPR	Accuracy
fp32 (parent)	0.9751	0.9751	0.9751	0.013	0.983
int8	0.9740	0.9723	0.9757	0.015	0.982
int8_int4_embeddings	0.9754	0.9746	0.9763	0.013	0.983

Files

• onnx/fp16/model.onnx, onnx/int8/model.onnx, onnx/int8_int4_embeddings/model.onnx
• config.json, tokenizer.json, tokenizer_config.json (from the parent)
• metrics/quant_bench.json

License

This model is released under the Apache License 2.0. A copy of the license is included as LICENSE in this repository.

Patronus Ark

This model is built to run inside Patronus Ark, Patronus' open-source on-device AI-security scanning library (L1 native rules → L2 NTDB cascade → L3 transformer). Ark is not publicly released yet — a repository link will be added here at launch.

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