---

license: gemma

base_model: lmstudio-community/gemma-4-12B-it-QAT-GGUF

pipeline_tag: text-generation

library_name: llama.cpp

language:

- en

tags:

- gguf

- llama.cpp

- gemma

- factbank

- security

- secure-coding

- appsec

- cwe

- reasoning

---

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)

| edition | file | when to use | notes |

|---|---|---|---|

| thinking-OFF (default) | gemma-4-12B-security-expert-Q4_0.gguf | production, speed, reliability | 0 empty answers, fast, secure. No reasoning trace. The safe default. |

| thinking-ON | gemma-4-12B-security-expert-thinking-Q4_0.gguf | you want a visible reasoning trace | Shows its work and is strong on most tasks, *but ~10% of the hardest-reasoning prompts can run long and return a blank answer* (fails safe — it does not emit insecure code; just retry). Needs a large context + generous output budget. |

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.

| model (baked, thinking-off) | secure / 21 |

|---|---|

| e2b base | 11 |

| e2b + bank | 13 |

| 12b base | 17 |

| 12b + bank | 17 |

| 26b base | 17 |

| 26b + bank | 19 — best |

| DeepSeek-V4 (cloud) | 14 |

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.