Model
Galvanize-60M
The open-weights prompt-injection classifier behind the zn Cloud API, and how it was evaluated.
Galvanize-60M is zn's prompt-injection classifier for agent traffic. It is a 4-layer slice of answerdotai/ModernBERT-base (60M parameters) that keeps native rotary positions for inputs up to 8,192 tokens and runs in about 11.52 ms p50 on a standard CPU.
- Weights:
usezn/Galvanize-60Mon Hugging Face, Apache-2.0 (INT8 ONNX atonnx/model_quantized.onnx). - Benchmark data:
zn-prompt-injection-bench(23,699 rows, CC-BY-4.0). - Background: Introducing Galvanize-60M.
Security pooling
Generic sentence embeddings confuse ordinary JSON, SQL and code in tool arguments with attacks. Galvanize-60M replaces CLS or mean pooling with four learned query vectors, concatenated into a 3,072-dimensional representation:
| Query | Focus |
|---|---|
| 0 | Instruction overrides and privilege escalation |
| 1 | Persona, role-play and hypothetical framing |
| 2 | Legitimate JSON, XML and Markdown versus delimiter escapes |
| 3 | Attempts to leak environment variables, memory or tokens |
Benchmark
First-party evaluation published on 6 September 2026. It is reproducible with the public dataset, but it has not been run by an independent party.
| Metric | Galvanize-60M | Prompt-Guard-2-86M | Prompt-Guard-2-22M | ProtectAI DeBERTa-v3 |
|---|---|---|---|---|
| Tool false-positive rate | 1.00% (conservative headline; 0.67% measured at threshold 0.80) | 5.00% | 0.00% | 90.33% |
| Out-of-distribution recall (deepset) | 91.60% (calibrated) | 9.58% | 3.75% | 20.42% |
| Long-context needle recall | 77.00% to 97.00% | 7.00% | 0.00% | 1.00% |
| Adversarial robustness (attacks generated by our internal red-team model) | 94.00% blocked | 70.00% | 26.00% | 82.00% |
| CPU latency, p50 | 11.52 ms (INT8: 18.18 ms) | 45.36 ms | 17.64 ms | 55.79 ms |
The Cloud API runs the model at a stricter production threshold (0.95). See the comparison page for how to read vendor claims, including ours.
Self-hosting
Load the ONNX file with ONNX Runtime and the tokenizer from the same repository. For most teams the Cloud API is simpler; self-hosting makes sense when data cannot leave your network.