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

Model Card for Wolf Defender Prompt-Injection Detector Edge

Edge/quantized ONNX variant of patronus-studio/wolf-defender-prompt-injection. 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; this repo only adds the small variants. Binary prompt-injection detector (mmBERT-base).

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)	—	0.9395	0.9454	0.9336	0.036	0.952
int8	309 MB	0.9406	0.9437	0.9376	0.037	0.953
int8_int4_embeddings	218 MB	0.9410	0.9465	0.9356	0.035	0.953

Recommended: onnx/int8_int4_embeddings/model.onnx — ~5.6× 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)	0.9971	0.9965	0.9977	0.002	0.998
int8	0.9974	0.9971	0.9977	0.002	0.998
int8_int4_embeddings	0.9960	0.9948	0.9971	0.003	0.997

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

Usage

import onnxruntime as ort, numpy as np
from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("patronus-studio/wolf-defender-prompt-injection-edge")
sess = ort.InferenceSession("onnx/int8_int4_embeddings/model.onnx", providers=["CPUExecutionProvider"])
enc = tok("your text", truncation=True, max_length=2048, padding="longest", return_tensors="np")
logits = sess.run(["logits"], {"input_ids": enc["input_ids"].astype(np.int64),
                               "attention_mask": enc["attention_mask"].astype(np.int64)})[0]
pred = int(logits.argmax(1)[0])  # 1 = injection

🛡️ Patronus Protect

Brought to you by Patronus Protect — a local AI firewall that secures every AI interaction (prompts, tools, documents) before it reaches your models. Try it for free at patronus.studio.

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.