Dataset: https://huggingface.co/datasets/dbristol/aisec-training-data)

Llama.cpp model: https://huggingface.co/dbristol/aisec_model_v1)

license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 base_model_relation: finetune dbristol: - mlx - lora - mistral - ai-security - nist-ai-rmf - mitre-atlas - owasp-ai-exchange - google-saif - risk-management - fine-tuned language: - en pipeline_tag: text-generation datasets: - dbristol/aisec-training-data

library_name: mlx

aisec_model_v1 — AI Security Framework Expert (Mistral 7B LoRA)

This is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3, not a new model architecture. Only 0.145% of parameters were updated via LoRA. The base model weights, tokenizer, and architecture are unchanged.

Domain-specialised using LoRA on Apple Silicon via MLX for cross-framework AI security and risk management analysis across:

• NIST AI RMF 1.0 — Govern, Map, Measure, Manage functions
• MITRE ATLAS — Adversarial TTP kill chains and detection engineering
• OWASP AI Exchange — Runtime attack surfaces and technical controls
• Google SAIF — Component responsibility assignment and governance layers

Model Details

Training Summary

Training was performed using mlx_lm.lora with a cosine learning rate schedule.

Training configuration:

learning_rate: 5e-5
lr_schedule: cosine_decay (100-iter warmup)
batch_size: 4
iters: 1200
lora_rank: 8
lora_alpha: 16.0
lora_dropout: 0.05
num_layers: 16

Usage

Requirements

pip install mlx-lm

Inference with MLX

from mlx_lm import load, generate

model, tokenizer = load(
    "Dbristol/aisec_model_v1"
)

prompt = "Provide a cross-framework analysis of indirect prompt injection defences \
for a code generation assistant using OWASP AI Exchange, SAIF, MITRE ATLAS, \
and NIST AI RMF."

messages = [
    {
        "role": "system",
        "content": (
            "You are an expert AI security and risk management assistant "
            "specialising in NIST AI RMF 1.0, MITRE ATLAS, OWASP AI Exchange, "
            "and Google SAIF frameworks."
        )
    },
    {"role": "user", "content": prompt}
]

formatted = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

response = generate(
    model,
    tokenizer,
    prompt=formatted,
    max_tokens=512,
    temp=0.4,
    top_p=0.85,
)
print(response)

Recommended inference parameters

Intended Use

This model is designed for security practitioners, researchers, and AI governance professionals who need structured cross-framework analysis. Suitable use cases include:

• Mapping AI system risks across multiple frameworks simultaneously
• Generating NIST AI RMF governance documentation
• Identifying MITRE ATLAS TTPs relevant to a specific AI deployment
• Drafting OWASP AI Exchange control implementations
• Cross-referencing Google SAIF responsibility assignments

Out-of-scope use

This model should not be used as the sole basis for security decisions without human expert review. Framework guidance evolves; always verify against current official documentation.

Limitations

• Trained on a single-domain dataset; may underperform on security tasks outside the four covered frameworks.
• Knowledge cutoff reflects the training data collection date, not live framework updates.
• Responses should be verified against official NIST, MITRE, OWASP, and Google SAIF publications before operational use.
• Base model is Mistral 7B Instruct v0.3; inherits its general limitations.

License

This model is released under Apache 2.0.

The base model (Mistral-7B-Instruct-v0.3) is also Apache 2.0 licensed.

The training dataset is derived from publicly available framework documentation. See the dataset card for full provenance and source attribution.

Citation

If you use this model in research or production, please cite:

@misc{aisec_model_v1,
  author    = {dbristol},
  title     = {aisec\_model\_v1: Mistral 7B Fine-Tuned for AI Security Framework Analysis},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/dbristol/aisec_model_v1}
}