README.md

metadata

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

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:

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

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:

@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}
}