15,023 exploit code files (Python, C, Perl, Ruby, PHP) from ExploitDB
13,468 vulnerability detection rules from Nuclei templates
576 attack payloads from PayloadsAllTheThings (SQLi, XSS, CMDi, SSTI, SSRF, etc.)
523 methodology & reference documents on exploitation techniques

It's designed to assist with legitimate penetration testing, vulnerability research, and security education.

Quick Start

Option 1: Merge + run locally (recommended)

bash

# 1. Download the LoRA adapter
git lfs install
git clone https://huggingface.co/ezradixon/ZeroSec-7B

# 2. Merge with base model
python merge_and_export.py

# 3. Quantize for local inference (Mac/CPU)
llama-quantize merged-model-f16.gguf Q4_K_M zerosect-7b-Q4_K_M.gguf

# 4. Run
llama-server -m zerosect-7b-Q4_K_M.gguf -ngl 99

Option 2: Python inference

python

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

base_model = "Qwen/Qwen2.5-Coder-7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, "ezradixon/ZeroSec-7B")
model = model.merge_and_unload()

tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)

prompt = "Write a Python script to check for SQL injection in a login form"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Parameter	Value
Base model	Qwen/Qwen2.5-Coder-7B-Instruct
Method	QLoRA (4-bit quantization)
GPU	NVIDIA A10G (24GB)
Training samples	29,588
Max sequence length	1024 tokens
Batch size	2 (effective 8 with grad accum)
Epochs	2
Learning rate	2e-4
LoRA rank	16, alpha 32
Training time	~2 hours
Cost	~$2.00 (Modal serverless)

Limitations

7B parameters — not as capable as larger models (GPT-4, Claude)
Training data quality varies — ExploitDB contains legacy code that may not work on modern systems
No real-world validation — exploits were not tested against live targets
English only — training data is overwhelmingly English

⚠️ Disclaimer

This model is for authorized security testing and education only.

Do not use ZeroSec-7B for:

Attacking systems without explicit written permission
Developing malware or ransomware
Any illegal activity whatsoever

The Zero Project and the model's creator are not responsible for any misuse.

Author

Ezra Dixon — The Zero Project

Built on Modal cloud GPUs. 🇦🇺

HeeBive/ZeroSec-7B

ZeroSec-7B 🔴

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