gemma-4-12B-security-expert-GGUF

gemma-4-12B-security-expert (GGUF)

A 12B model that writes secure code by default — an application-security FactBank is baked into its chat-template and fires inside the inference engine to steer the model off insecure-by-default patterns. Weights untouched; no external RAG. This repo ships two editions (see below).

🔗 Full project: github.com/mhndayesh/experts-models

Two editions — pick one (files in this repo)

Both editions carry the same bank. They differ only in whether chain-of-thought is on. We ship both so you know exactly what you're getting — a landmine fact reverses a trained habit, and with thinking on the model sometimes over-reasons back toward its prior, so the thinking edition trades some reliability for a reasoning trace. Thinking-ON has a known llama.cpp Gemma-4 fix applied (the generation prompt opens the thought channel) + strong authority framing so facts hold; the residual ~10% is inherent to reasoning-on.

Settings that work (baked into the Hub editions; set these yourself on llama.cpp)

• Sampling (gemma-native): temperature 1.0, top_k 64, top_p 0.95, min_p 0.01. The min_p 0.01 floor is important — it stops the low-temperature repetition loop that otherwise yields empty answers.
• Context length: ≥ 16k, 32k recommended (contextLength 32768 is baked). The bank injects ~3,400 tokens per query, so a small context truncates the answer — especially with thinking on.
• Output budget (thinking-ON): allow generous max_tokens (reasoning traces run ~2–3k tokens); too small a cap truncates before the answer.

What it does

Given a normal coding request it silently corrects insecure defaults, e.g. torch.load(weights_only=True) (post-cutoff), XXE resolve_entities=False, yaml.safe_load, secrets over random, os.environ creds, parameterized SQL, ast.literal_eval over eval, constant-time HMAC compare. You don't name the vulnerability — the bank is indexed by the coding task, so it fires on benign prompts.

Coverage — what's in the bank

• 258 concepts (254 CWE + 4 door groups) → 3,984 language/framework variants (organized by weakness, not a short lib list).
• 10+ languages: Python, Java, JavaScript, C/C++, Swift, Go, C#, Rust, Ruby, and more.
• Frameworks: Android, iOS/SwiftUI/WKWebView, Flask, Django, Express, Node.js, ASP.NET/.NET, Spring, Java EE, Rails, Laravel.
• 7 mined sources: MITRE CWE, GitHub CodeQL, SAST (Bandit+gosec), OWASP MASTG, RustSec, NIST/RFC/Mozilla crypto-net, OWASP — each fact quote-grounded then adversarially correctness-audited (~3.8% wrong/stale removed).

How the three sizes compare — and vs a cloud model (hand-scored)

External SecurityEval (s2e-lab, MSR 2022, 121 Python CWE tasks). Every arm below is the shipped baked GGUF, thinking-OFF, served on my own llama-server with identical scoring; DeepSeek-V4 (deepseek-v4-flash) run on the same prompts in no-thinking mode (thinking disabled) — to match the thinking-off editions here (its reasoning mode could score higher; this is an apples-to-apples, thinking-off comparison). Table is the common pattern-checkable subset (21 tasks) where every arm is machine-judgeable.

Reading it: bigger model → more secure. The bank lifts each model and helps most where the base is weak

• on specific weaknesses — XXE (CWE-611): 12b 5→6/6, 26b 4→5/5, plus deserialization, weak-random, hardcoded-creds. 12b+bank and 26b+bank beat the cloud model; e2b+bank is about even with it. On each model's own full judgeable set the bank adds +2–3 (e2b 15→18, 12b 29→31, 26b 30→33).

Honest note: these are the in-template (prompt-only) baked editions. The FactBank retrieval method reaches higher when run as a served loop with a HyDE draft-key + authority framing (e2b 14→25 on the 28-task subset) — that draft-key is the big lever for benign security prompts and is not in the baked template. The numbers above are what the shipped GGUFs actually do.

How to run

LM Studio (Hub editions — the full bank is delivered via model.yaml)

The bank template is 4.2 MB — over LM Studio's ~980 KB raw-GGUF cap, so for LM Studio use the Hub virtual models (they hand the full template to the engine, no cap), which also carry the settings above:

• thinking-OFF: https://lmstudio.ai/mhndayesh/gemma-4-12b-security-expert
• thinking-ON: https://lmstudio.ai/mhndayesh/gemma-4-12b-security-expert-thinking

llama.cpp / any OpenAI-compatible server (this GGUF, full bank embedded)

# thinking-OFF (recommended)
llama-server -m gemma-4-12B-security-expert-Q4_0.gguf --jinja --ctx-size 32768 --port 8080
# thinking-ON
llama-server -m gemma-4-12B-security-expert-thinking-Q4_0.gguf --jinja --ctx-size 32768 --port 8080

Sampling: temperature 1.0, top_k 64, top_p 0.95, min_p 0.01. Don't quantize the KV cache on small models. Exposes an OpenAI-compatible API at /v1.

Limitations

• Scoped to secure coding. Outside it, base model.
• Supplies knowledge, not reasoning — still misses some multi-step fixes.
• thinking-ON: ~10% of hard prompts run long / blank (fails safe). Use thinking-OFF for reliability.
• Keyword + faceted retrieval, not embeddings — an unusual phrasing can miss. Research-grade; review output.

Provenance & license

• Base: lmstudio-community/gemma-4-12B-it-QAT-GGUF (weights untouched). Only tokenizer.chat_template is rewritten to embed an inverted-index retriever + the bank (factbank.version 0.4.0).
• License: Google Gemma Terms of Use (license: gemma). Fact bank = FactBank-project content; mined sources keep their own licenses.
• Source & method: github.com/mhndayesh/experts-models.