---
license: apache-2.0
base_model: XHToken/Spark-X2.5-4B
tags:
- decision-model
- system-one
- gguf
- llama-cpp
- security
- triage
library_name: gguf
---

# jevsec — a fine-tuned decision model for security triage

Current release: **jevsec-003** (file `jevsec-003-q8_0.gguf`; the second iteration stays in the
repository history). A LoRA fine-tune of [XHToken/Spark-X2.5-4B](https://huggingface.co/XHToken/Spark-X2.5-4B)
(Apache-2.0) that answers **typed decisions with probabilities** on the System One wire contract
(`POST /v1/systemone`: `noul`, `choice`, `score`), trained to support two tasks in authorized
security work:

- **finding triage** — verdict (true_positive / false_positive / needs_review) and pre-auth
  reachability, on redacted scanner findings;
- **observation prioritization** — atomic questions about engagement observations (credentials
  exposed? privilege? reachability? escalation path?), recombined into impact scores by code,
  not by the model.

The model only ever sees **redacted text** (hex/base64 blobs replaced, bodies truncated) and
never sees the objective or scenario words: it judges facts.

## Serving

Any System One-compatible runtime on top of llama.cpp can serve this GGUF and expose
`/v1/systemone`; JEVSEC (`github.com/s3m3y4z4/jevsec`) points its `base_url` at it. One forward
pass per question set, zero generated tokens.

## Training

- LoRA r=16, alpha=32, all attention/MLP linear layers, lr 5e-5, 8 epochs, bf16, merged into
  the base weights. Hyperparameters are identical across iterations: only the data changes.
- Data: a private, de-identified dataset of 317 security observations with **human-verified
  labels** (68 operator verdicts where the operator's judgment overrides the model's),
  documentation-reserved IPs/domains only, scenario words filtered out, content-deduplicated.
  The third iteration absorbs field feedback from two King-of-the-Hill engagements, including
  verdicts that corrected the previous model on external intel and duplicated facts.
- Measured on a held-out dev set: decision accuracy 0.81 (majority baseline ~0.56),
  prioritization accuracy 0.79.

### Training curves

![training loss](training-loss.png)

![dev-set accuracy](training-accuracy.png)

## Limitations — read before deploying

- **Probabilities are not calibrated** on your domain: temperature scaling on your own labeled
  data is required before any threshold-based decision.
- **Prompt injection moves the model**: injected instructions in finding bodies can push
  probabilities (measured). This model is intended for **human-in-the-loop triage**; never wire
  it to automatic actions. JEVSEC ships with its auto-gate disabled for exactly this reason.
- **Small training set** (317 examples): treat it as an experiment that learns operator
  judgment, not as a production classifier. Validate on your own material first.
- English observations only.

## Intended use

Decision support for **authorized** security testing (engagements you are permitted to test,
CTF/King-of-the-Hill practice). The model sorts and explains; the operator decides.

## License

Apache-2.0 (base model: XHToken/Spark-X2.5-4B, Apache-2.0; fine-tuning and redistribution
permitted).