Downloads · 30 days
19
21% of all-time downloads
wagonbomb/sigmashift-suricata-v1
sigmashift-suricata-v1 is a text generation model from wagonbomb. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
A fine-tuned Mistral-7B model that generates production-ready Suricata IDS rules and Zeek detection scripts from security findings (CVEs, vulnerability descriptions, threat intelligence).
Downloads · 30 days
19
21% of all-time downloads
All-time downloads
91
Public
Repo size
5 GB
Likes
0
Public
Click a slice to open those files.
.gguf4.4 GB · 87%
From the Hugging Face model README
A fine-tuned Mistral-7B model that generates production-ready Suricata IDS rules and Zeek detection scripts from security findings (CVEs, vulnerability descriptions, threat intelligence).
| Parameter | Value |
|---|---|
| Base model | mistralai/Mistral-7B-Instruct-v0.3 |
| Method | QLoRA (rank=64, alpha=128, NF4 quantization) |
| Training | 3 epochs, 1266 steps, batch 4, lr 2e-4 |
| Final train loss | 0.106 |
| Best eval loss | 0.141 (checkpoint 1200) |
| Quantization | Q4_K_M (4.1GB) via llama.cpp |
Tested on 20 held-out CVE test cases (not in training data):
Scoring criteria: block presence (suricata/zeek/reasoning), valid action keyword, msg field, SID, classtype, rev, balanced parentheses, Notice redef, event handler.
# Download the GGUF
# Create model from Modelfile.q4km (update the FROM path)
ollama create sigmashift-suricata:v1-q4km -f Modelfile.q4km
# Run
ollama run sigmashift-suricata:v1-q4km "Generate a Suricata rule for CVE-2021-44228 Log4Shell"
The model outputs three tagged blocks:
<suricata>
alert tcp $HOME_NET any -> $EXTERNAL_NET $HTTP_PORTS (msg:"ET EXPLOIT Apache Log4j RCE (CVE-2021-44228)"; ...)
</suricata>
<zeek>
@load base/frameworks/notice
redef enum Notice::Type += { Log4Shell_Attempt };
event http_message_done(c: connection, is_orig: bool) { ... }
</zeek>
<reasoning>
Detection approach explanation...
</reasoning>
| File | Description |
|---|---|
sigmashift-suricata-v1-q4km.gguf | Q4_K_M quantized model (4.1GB) |
Modelfile / Modelfile.q4km | Ollama model definitions with system prompt |
adapter/ | QLoRA adapter weights (for custom merging) |
eval_report.md | Full 20-case evaluation report |
Built from:
This model powers the offline/air-gapped rule generation mode in SigmaShift, a security analysis platform that converts vulnerability scan output into detection rules.
Apache 2.0