README.md

metadata

base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: peft
tags:
  - cybersecurity
  - malware-analysis
  - peft
  - lora
  - qlora
  - mixtral
language:
  - en
pipeline_tag: text-generation
license: apache-2.0

Fathom Plan A LoRA Adapter (Mixtral-8x7B-Instruct)

This repository contains the Plan A LoRA adapter for the Fathom FYP project:

"Fathom: An LLM-Powered Automated Malware Analysis Framework"

The adapter is trained on a curated cybersecurity instruction-tuning corpus to improve analyst-style security outputs over the base mistralai/Mixtral-8x7B-Instruct-v0.1 model.

What This Is

• Type: PEFT LoRA adapter (not a full standalone model)
• Base model required: mistralai/Mixtral-8x7B-Instruct-v0.1
• Training style: QLoRA (4-bit NF4 base loading, bf16 compute)
• Scope: Plan A MVP uplift for cybersecurity and malware-analysis assistance

Key Training Setup

• Sequence length: 2048
• Batch: 2
• Gradient accumulation: 8 (effective 16)
• Learning rate: 2e-4 (cosine scheduler)
• Steps: 3000 (completed run)
• LoRA rank/alpha: r=32, alpha=64
• LoRA targets: q_proj, k_proj, v_proj, o_proj (attention-only)
• Optimizer: paged_adamw_8bit
• Precision: bf16

Hardware Used

Training was run on RunPod:

• GPU: NVIDIA A100 PCIe 80GB (1x)
• vCPU: 8
• RAM: 125 GB
• Disk: 200 GB
• Location: CA

Data Summary

Curated cybersecurity instruction corpus with mixed sources (CyberMetric, Trendyol CyberSec, ShareGPT Cybersecurity, NIST downsampled, MITRE ATT&CK, CVE/IR/malware-focused sets).

Final working files used:

• train.jsonl: 120,912 samples
• eval.jsonl: 1,915 samples
• cybermetric_80.jsonl: 80 held-out MCQs
• malware_eval_25.jsonl: 25 expert malware prompts

Evaluation Results

Standard post-eval settings

Generation settings used for fair base-vs-adapter comparison:

• do_sample=False
• temperature=0.0
• max_new_eval=64
• max_new_cyber=48
• max_new_malware=256

Baseline (corrected) vs Fine-tuned

Metric	Baseline	Fine-tuned	Delta
Eval mean overlap	0.3283	0.3631	+0.0349
Eval exact match rate	0.0000	0.2193	+0.2193
CyberMetric-80 accuracy	0.825	0.900	+0.075
Malware structure	0.44	0.84	+0.40
Malware ATT&CK correctness	0.16	0.20	+0.04
Malware reasoning	0.24	0.20	-0.04
Malware evidence awareness	0.48	0.52	+0.04
Malware analyst usefulness	0.52	0.56	+0.04

Malware-only rerun with longer output budget

To test truncation effects on malware prompts, both base and fine-tuned were rerun with max_new_malware=512 (25 prompts only).

Rubric axis	Base (512)	Fine-tuned (512)	Delta
Structure	0.56	0.88	+0.32
ATT&CK correctness	0.16	0.20	+0.04
Malware reasoning	0.36	0.28	-0.08
Evidence awareness	0.56	0.64	+0.08
Analyst usefulness	0.64	0.80	+0.16

Interpretation: structure/evidence/usefulness improved strongly, but malware reasoning remains the main gap for future iterations.

Limitations

• This is a Plan A MVP adapter, not a fully specialized malware reverse-engineering model.
• Malware causal reasoning still needs improvement via targeted data and/or evidence-grounded training (Plan B).
• Outputs should be treated as analyst assistance, not an autonomous verdict.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

base_model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
adapter_repo = "umer07/fathom-mixtral-lora-plan-a"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
)

tokenizer = AutoTokenizer.from_pretrained(base_model_id, use_fast=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    device_map={"": 0},
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
)

model = PeftModel.from_pretrained(model, adapter_repo)
model.eval()

prompt = """### Instruction:
Analyze the malware behavior and map likely ATT&CK techniques.

### Input:
Sample creates scheduled task persistence and launches encoded PowerShell.

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=512, do_sample=False, temperature=0.0)

print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Project Status

• Core Plan A training/evaluation cycle: completed
• GPU instance used for training has been deleted
• No additional training is currently in progress

Citation

If you use this adapter, please cite your project report/thesis for Fathom Plan A and reference the base model (mistralai/Mixtral-8x7B-Instruct-v0.1).