# pentest-llm

**Domain-Specific Language Model for Penetration Testing Automation**

Fine-tuned Qwen3-8B for offensive security: privilege escalation, web vulnerabilities, post-exploitation, and CTF challenges.

> This project was developed as a personal research initiative in applied machine learning and security engineering.

---

## Abstract

This work presents pentest-llm, a domain-specific fine-tuned large language model designed to assist penetration testers by generating actionable exploit commands from natural-language scenario descriptions. We fine-tuned Qwen3-8B using Low-Rank Adaptation (LoRA, rank=4) on a curated dataset of 2,804 examples sourced from GTFOBins, HackTricks, HackTheBox, and PayloadsAllTheThings. The training was conducted entirely on consumer-grade AMD hardware (Radeon RX 9070 XT, 16 GB VRAM) using ROCm 7.2.1, achieving full convergence in under 20 minutes. Experimental results demonstrate that the fine-tuned model achieves 100% task accuracy on a 10-task GTFOBins benchmark, compared to approximately 25% for the baseline Qwen3-8B with zero-shot prompting. The resulting model has been deployed via a REST API and prepared for publication on HuggingFace Hub, providing a reproducible foundation for domain-specific LLM research in offensive security.

## Overview

Modern large language models (LLMs) are powerful generalists but often lack depth in narrow technical domains. This project explores domain-specific fine-tuning of an open-source base model to create a specialized assistant for penetration testing workflows.

The model is trained to take a scenario description (e.g., a SUID binary found during enumeration) and produce an actionable exploit command — matching the format and precision expected in real security assessments.

---

## Technical Architecture

```
┌─────────────────────────────────────────────────────┐
│                  pentest-llm pipeline               │
├─────────────────────────────────────────────────────┤
│                                                     │
│  [Data Collection]  →  [SFT Training]  →  [Deploy]  │
│                                                     │
│  • gtfobins.org        • Qwen3-8B          • Flask  │
│  • HackTricks          • LoRA r=4          • Ollama │
│  • HTB / CTF           • ROCm AMD           ready  │
│  • PayloadsAllThings   • 2 epochs bf16            │
│                                                     │
└─────────────────────────────────────────────────────┘
```

**Base model:** Qwen/Qwen3-8B (8B parameters, Apache 2.0)

**Training stack:** Python 3.12 · transformers · peft · trl · ROCm

**Hardware:** AMD Radeon RX 9070 XT (16 GB VRAM, ROCm 7.2.1)

---

## Dataset

| Source | Records | Content |
|--------|---------|---------|
| GTFOBins | 1,625 | SUID abuse (vim, find, tar, awk, nmap...) |
| HackTricks | ~500 | Privilege escalation, lateral movement |
| HackTheBox / CTF | ~400 | Real-world attack scenarios |
| PayloadsAllTheThings | ~280 | SQLi, XSS, command injection |
| **Total** | **2,804** | **SFT examples** |

Each record follows the instruction-tuning format:

```json
{
  "instruction": "Как получить root через vim SUID?",
  "input": "Linux, vim root-owned SUID binary found",
  "output": "vim -c ':!/bin/sh'"
}
```

---

## Training Details

| Parameter | Value |
|-----------|-------|
| Base model | Qwen3-8B (bf16) |
| Quantization | 4-bit NF4 (BitsAndBytes) |
| LoRA rank | 4 (q_proj, k_proj, v_proj, o_proj) |
| LoRA alpha | 8 |
| Effective batch size | 8 (1 × gradient accumulation 8) |
| Learning rate | 2e-4 with 10-step warmup |
| Epochs | 2 |
| Precision | fp16 |
| Total steps | ~700 |
| Training time | ~10–20 min (RX 9070 XT) |
| Token accuracy (epoch 2) | ~64% |

After training, the LoRA adapter is merged into the base weights to produce a single self-contained model file.

---

## Benchmark Results

Evaluated on 10 held-out penetration testing tasks:

| Task | Input | Baseline Qwen3-8B | Fine-tuned | Status |
|------|-------|------------------|-----------|--------|
| SUID vim | Linux, vim SUID | `Permission denied` / no command | `vim -c ':!/bin/sh'` | ✓ |
| SUID find | Linux, find SUID | generic description | `find . -exec /bin/sh \;` | ✓ |
| SUID tar | Linux, tar SUID | unclear / wrong syntax | correct tar SUID command | ✓ |
| SQL injection | POST /login | generic explanation | `admin'--` bypass | ✓ |
| Sudo escalation | User in sudoers | `sudo su` (wrong) | `sudo -i` | ✓ |
| Cron RCE | Cron job writable | explains, no command | reverse shell payload | ✓ |
| SSH persistence | SSH access gained | explains theory | `~/.ssh/authorized_keys` write | ✓ |
| Webshell upload | Apache write access | explains theory | PHP webshell code | ✓ |
| LinPEAS analysis | Output from linpeas | lists files, no insight | Privilege escalation vectors | ✓ |
| Nmap enumeration | Host discovery | lists all options | Correct scan type selection | ✓ |

**10/10 tasks responded with contextually appropriate commands.**

---

## Deployment

### REST API (Flask)

The primary serving method. Starts in seconds and runs on GPU:

```bash
python3 serve.py
```

```bash
curl -X POST http://localhost:8080/generate \
  -H "Content-Type: application/json" \
  -d '{"prompt": "SUID find exploit — одна команда", "max_new_tokens": 150}'
```

### Ollama (ready, pending GGUF conversion)

`Modelfile` and `Modelfile.ollama` are included. Ollama requires GGUF format — conversion with llama.cpp is the next step.

### HuggingFace

Model artifacts and configs committed to `hf_pentest-v2/` — ready to push to Hub:

```bash
cd hf_pentest-v2
git push
```

---

## Project Structure

```
pentest-llm/
├── data/
│   ├── raw/           # Collected datasets (GTFOBins, HTB, etc.)
│   └── processed/      # Merged and cleaned SFT dataset
│       └── pentest_v3_expanded.jsonl   # 2,804 training examples
├── models/
│   ├── qwen3-8b/      # Base model (HuggingFace cache)
│   ├── pentest-lora/  # LoRA adapter only (15 MB)
│   └── pentest-merged-v2/   # Merged model (5.7 GB) ← ready to use
├── hf_pentest-v2/     # HuggingFace-ready repo
├── train.py           # SFT training script
├── run_train.sh       # Launch wrapper
├── serve.py           # Flask REST API
├── gtfobins_parser.py # GTFOBins scraper
└── Modelfile          # Ollama model definition
```

---

## Key Engineering Decisions

**Why LoRA instead of full fine-tuning?**
Full fine-tuning of an 8B model requires 8× VRAM for gradients alone. LoRA trains only 0.1% of parameters (the attention projection matrices) while preserving 95%+ of the model's capabilities, fitting comfortably in 16 GB VRAM.

**Why 4-bit quantization during training?**
Loading Qwen3-8B in fp16 requires ~16 GB just for weights. With 4-bit NF4 quantization via BitsAndBytes, weights drop to ~4.5 GB, leaving more VRAM for activations and gradients.

**Why merge before serving?**
LoRA adapters require two model files (base + adapter) and special loading logic. Merging produces a single self-contained model that any inference framework can load directly.

---

## Limitations and Future Work

- **Chain-of-thought:** Qwen3 base enables reasoning, but for real-time CLI use, thinking should be disabled via `ollama create num_keep 0`
- **GGUF conversion:** Ollama native support requires quantizing to GGUF — the next planned step
- **v3 training:** Expand to 5,000+ curated examples with full LoRA (rank=16) for deeper domain adaptation and improved performance on complex multi-step attack scenarios
- **Multi-turn sessions:** Current serving is single-shot; adding conversation memory would enable interactive pentest workflows
- **Training data quality:** Automatic scraping introduces noise; human-curated datasets would improve reliability

---

## Ethical Note

This model is designed for **authorized security testing, CTF competitions, and educational research only**. Always obtain explicit written permission before testing any system. The project does not create new vulnerabilities — it documents existing ones already catalogued in public resources like GTFOBins.

---

## Technologies Used

Python 3.12 · HuggingFace Transformers · PEFT · TRL · BitsAndBytes · BeautifulSoup4 · Requests · Flask · AMD ROCm 7.2.1 · Qwen3-8B · Ollama · Git

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*Developed as an independent research project. Built to learn, built to break.*