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dbristol/
aisec_model_v1

Name: aisec_model_v1
Availability: InStock

dbristol/aisec-training-data

mistralai/Mistral-7B-Instruct-v0.3

finetune

apache-2.0

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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

Property	Value
Base model	mistralai/Mistral-7B-Instruct-v0.3
Fine-tuning method	LoRA (Low-Rank Adaptation)
Framework	MLX (Apple Silicon)
Trainable parameters	10.486M / 7,248M (0.145%)
LoRA rank	8
LoRA alpha	16
LoRA layers	16
Training platform	Apple Silicon (M-series), macOS
Best checkpoint	Iter 500 (val loss 0.216)
Training dataset	dbristol/aisec-training-data

Training Summary

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

Checkpoint	Val Loss
Iter 1 (base)	2.597
Iter 100	0.749
Iter 200	0.369
Iter 300	0.312
Iter 400	0.267
Iter 500	0.216 ← best
Iter 550	0.223 ↑ overfitting onset

Training configuration:

yaml

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

python

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

Parameter	Value	Rationale
temperature	0.4	Factual domain — sharper distribution favours trained signal
top_p	0.85	Tighter nucleus reduces long-tail sampling
top_k	40	Hard vocabulary cap applied before top_p
repeat_penalty	1.1	Reduces repetition of framework acronyms

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:

bibtex

@misc{aisec_model_v1,
  author    = {<your-name>},
  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}
}