openparallax/shield-classifier-v1

OpenParallax Shield Classifier v1

Fine-tuned DeBERTa-v3-base for prompt injection detection in AI agent tool calls.

Performance

Tested against 321 adversarial payloads across 6 attack categories:

Per-Category Results

Training

• —Base model: ProtectAI/deberta-v3-base-prompt-injection-v2
• —Training data: 6,787 samples (red-team payloads + agent-specific benign actions + NeurAlchemy dataset)
• —Epochs: 3
• —Hardware: Google Colab T4 GPU

Optimized for detecting injections in:

• —Tool call arguments (file paths, shell commands, HTTP requests)
• —Authority spoofing ("system override", "admin approved", tool impersonation)
• —Encoding evasion (base64, hex, URL encoding, Unicode homoglyphs, bidirectional text)
• —Multilingual injection (Spanish, Chinese, Russian, Arabic, Japanese, Korean, and more)

Usage with OpenParallax Shield

openparallax get-classifier

Usage with ONNX Runtime (Node.js)

import * as ort from "onnxruntime-node";
import { Tokenizer } from "tokenizers";

const session = await ort.InferenceSession.create("model.onnx");
const tokenizer = Tokenizer.fromFile("tokenizer.json");

const encoded = await tokenizer.encode("your text here");
const inputIds = new ort.Tensor("int64", BigInt64Array.from(encoded.getIds().map(BigInt)), [1, encoded.getIds().length]);
const attentionMask = new ort.Tensor("int64", BigInt64Array.from(encoded.getAttentionMask().map(BigInt)), [1, encoded.getAttentionMask().length]);

const results = await session.run({ input_ids: inputIds, attention_mask: attentionMask });
// logits[0] = SAFE probability, logits[1] = INJECTION probability

License

Apache 2.0