jcanode/safeguard-ministral3-3b

SafeGuard Ministral-3B — Prompt Injection Classifier

A LoRA-adapted Ministral-3-3B-Instruct model fine-tuned for binary prompt injection detection. Achieves 99.08% accuracy on a clean held-out test set at a total training cost of ~$4.40.

Performance (Clean Test Set, n=5,425)

On organic (non-synthetic) test data: 99.89% accuracy, 0 false positives.

Baseline Comparison

Fine-tuning a 3B model outperforms zero-shot prompting on a model 6x its size.

Training Details

• —Base model: mistralai/Ministral-3-3B-Instruct-2512-BF16
• —Method: LoRA (r=16, alpha=32, all 7 projection layers)
• —Trainable params: 24.7M (0.72% of 3.45B base)
• —Training data: 97,950 samples (clean, contamination-free)
• —Epochs: ~2 on RunPod A40
• —Total compute cost: ~$4.40

Technical Report

See SAFEGUARD_REPORT.pdf for the full technical report (v7.0) including:

• —Three training runs with detailed diagnostics
• —Benchmark contamination discovery and remediation
• —Comprehensive baseline evaluations
• —Error analysis and confidence intervals
• —Infrastructure and cost breakdown

Dataset

jcanode/safeguard-prompt-injection

Citation

@misc{safeguard2026,
  title={SafeGuard: A Dataset and Fine-Tuning Pipeline for Prompt Injection Detection},
  author={Canode, Justin},
  year={2026},
  url={https://huggingface.co/jcanode/safeguard-ministral3-3b}
}