elhayefrat/offensive_ollma

BaronLLM Llama 3.1 v1 (Q6_K GGUF)

A specialized Llama 3.1 model fine-tuned for offensive security operations and cybersecurity research

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📋 Model Overview

BaronLLM is a specialized version of Meta's Llama 3.1 8B model, fine-tuned and optimized for offensive security operations, penetration testing, and cybersecurity research. This Q6_K quantized GGUF version offers an excellent balance between model quality and computational efficiency.

Key Features

• 🎯 Specialized Training: Fine-tuned on offensive security scenarios and penetration testing methodologies
• ⚡ Optimized Performance: Q6_K quantization provides ~95% of full model quality with significantly reduced memory usage
• 🔧 Ready to Deploy: Compatible with llama.cpp, Ollama, LM Studio, and other GGUF-compatible inference engines
• 🛡️ Security Focused: Trained on CTI (Cyber Threat Intelligence) data and security frameworks
• 💻 Efficient Inference: Runs on consumer hardware with reasonable VRAM requirements

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🚀 Quick Start

Using with Ollama (Recommended)

Method 1: Direct Download from Hugging Face

# Step 1: Download the model
huggingface-cli download elhayefrat/offensive_ollma baronllm-llama3.1-v1-q6_k.gguf --local-dir . --local-dir-use-symlinks False

# Step 2: Create a Modelfile
cat > Modelfile << EOF
FROM ./baronllm-llama3.1-v1-q6_k.gguf

TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

{{ .Response }}<|eot_id|>"""

PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"
PARAMETER stop "<|reserved_special_token"
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER num_ctx 4096

SYSTEM """You are BaronLLM, an AI assistant specialized in offensive security, penetration testing, and cybersecurity research. You provide expert guidance on security testing methodologies, vulnerability analysis, and defensive countermeasures. Always emphasize authorized testing only."""
EOF

# Step 3: Create the model in Ollama
ollama create baronllm -f Modelfile

# Step 4: Run the model
ollama run baronllm

Method 2: Pull and Run (One Command)

If the model is already in your local directory:

# Create model from GGUF file
ollama create baronllm -f Modelfile

# Start chatting
ollama run baronllm "Explain the MITRE ATT&CK framework"

Interactive Usage Examples

# Example 1: Basic security question
ollama run baronllm "What are the phases of a penetration test?"

# Example 2: Vulnerability analysis
ollama run baronllm "Explain SQL injection and how to prevent it"

# Example 3: Tool guidance
ollama run baronllm "How do I use Nmap for network reconnaissance?"

# Example 4: MITRE ATT&CK mapping
ollama run baronllm "Map a typical ransomware attack to MITRE ATT&CK techniques"

# Example 5: Multi-turn conversation
ollama run baronllm
>>> What is a CVE?
>>> How do I search for CVEs related to Apache?
>>> What's the difference between CVE and CVSS?

Using Ollama API

# Start Ollama server (if not already running)
ollama serve

# Make API request
curl http://localhost:11434/api/generate -d '{
  "model": "baronllm",
  "prompt": "Explain the difference between white box and black box penetration testing",
  "stream": false
}'

Python Integration with Ollama

import ollama

# Simple query
response = ollama.generate(
    model='baronllm',
    prompt='What is the OWASP Top 10?'
)
print(response['response'])

# Streaming response
for chunk in ollama.generate(
    model='baronllm',
    prompt='Explain the kill chain methodology',
    stream=True
):
    print(chunk['response'], end='', flush=True)

# Chat with conversation history
messages = [
    {
        'role': 'system',
        'content': 'You are a penetration testing expert.'
    },
    {
        'role': 'user',
        'content': 'What tools should I use for web application testing?'
    }
]

response = ollama.chat(model='baronllm', messages=messages)
print(response['message']['content'])

Advanced Ollama Configuration

Create a custom Modelfile with specific parameters:

cat > Modelfile.advanced << EOF
FROM ./baronllm-llama3.1-v1-q6_k.gguf

# Llama 3.1 chat template
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

{{ .Response }}<|eot_id|>"""

# Stop tokens
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"
PARAMETER stop "<|reserved_special_token"

# Generation parameters
PARAMETER temperature 0.7        # Creativity (0.0 = deterministic, 1.0 = creative)
PARAMETER top_p 0.9             # Nucleus sampling
PARAMETER top_k 40              # Top-k sampling
PARAMETER repeat_penalty 1.1    # Penalize repetition
PARAMETER num_ctx 4096          # Context window size
PARAMETER num_predict 512       # Max tokens to generate

# System prompt for security focus
SYSTEM """You are BaronLLM, an elite cybersecurity AI assistant with expertise in:
- Offensive security and penetration testing
- Vulnerability analysis and exploitation
- MITRE ATT&CK framework
- Security tools (Metasploit, Burp Suite, Nmap, Wireshark)
- Threat intelligence and CTI
- Incident response and forensics
- Compliance and security frameworks

Provide detailed, practical guidance while always emphasizing:
1. Only perform authorized testing
2. Follow responsible disclosure
3. Comply with all applicable laws
4. Prioritize defensive measures"""
EOF

# Create the model with advanced configuration
ollama create baronllm-advanced -f Modelfile.advanced

# Run with advanced settings
ollama run baronllm-advanced

Ollama Model Management

# List all installed models
ollama list

# Show model information
ollama show baronllm

# Delete the model
ollama rm baronllm

# Pull updates (if published to Ollama library)
ollama pull baronllm

# Copy model with different name
ollama cp baronllm baronllm-backup

Using with llama.cpp

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Run inference
./main -m baronllm-llama3.1-v1-q6_k.gguf \
  -p "Explain the MITRE ATT&CK framework" \
  -n 512 \
  --temp 0.7 \
  --top-p 0.9

Using with LM Studio

1. Download the model file
2. Open LM Studio
3. Click "Import Model"
4. Select the baronllm-llama3.1-v1-q6_k.gguf file
5. Start chatting!

Python Integration

from llama_cpp import Llama

# Initialize model
llm = Llama(
    model_path="baronllm-llama3.1-v1-q6_k.gguf",
    n_ctx=4096,
    n_gpu_layers=35,  # Adjust based on your GPU
    n_threads=8
)

# Generate response
response = llm(
    "What are the phases of a penetration test?",
    max_tokens=512,
    temperature=0.7,
    top_p=0.9,
    echo=False
)

print(response['choices'][0]['text'])

---

🔧 Technical Specifications

Attribute	Details
Base Model	Meta Llama 3.1 8B
Model Type	Causal Language Model (Decoder-only Transformer)
Quantization	Q6_K (6-bit quantization with K-quants)
File Format	GGUF (GPT-Generated Unified Format)
File Size	~6.6 GB
Context Length	4,096 tokens (expandable to 8,192)
Vocabulary Size	128,256 tokens
Architecture	Transformer with Grouped-Query Attention (GQA)
Parameters	~8 billion (quantized)
Training Data	Security-focused datasets, CTI reports, penetration testing guides

Quantization Details

Q6_K Quantization uses a mixed quantization scheme:

• Most weights: 6-bit quantization
• Attention layers: Higher precision (8-bit)
• Output layer: Full precision where needed

Benefits:

• ✅ ~95% of original model quality retained
• ✅ ~50% reduction in memory usage vs FP16
• ✅ Faster inference on CPU and GPU
• ✅ Better quality than Q4/Q5 quantizations

---

💡 Use Cases

Offensive Security

• Penetration testing methodology guidance
• Vulnerability assessment strategies
• Exploit development concepts
• Red team operation planning

Defensive Security

• Security architecture review
• Incident response procedures
• Threat modeling and analysis
• Security control implementation

CTI & Research

• Threat actor analysis
• Malware behavior understanding
• MITRE ATT&CK technique mapping
• Security framework interpretation

Training & Education

• Security certification preparation
• Capture The Flag (CTF) guidance
• Security concept explanation
• Best practices education

---

📊 Performance Benchmarks

Hardware Requirements

Configuration	VRAM	RAM	Performance
Minimum	4GB GPU	8GB	5-10 tokens/sec
Recommended	8GB GPU	16GB	20-30 tokens/sec
Optimal	12GB+ GPU	32GB	40-60 tokens/sec

Speed Benchmarks

Tested on NVIDIA RTX 4090 with llama.cpp:

Context Size	Tokens/Second	Latency (first token)
512 tokens	58.3	45ms
2048 tokens	52.1	78ms
4096 tokens	47.8	145ms

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🎯 Model Capabilities

What This Model Does Well

✅ Security Framework Knowledge: MITRE ATT&CK, NIST CSF, CIS Controls
✅ Penetration Testing: Reconnaissance, exploitation, post-exploitation
✅ Vulnerability Analysis: CVE research, exploit techniques
✅ Network Security: Protocol analysis, traffic inspection
✅ Application Security: Web app testing, API security
✅ Malware Analysis: Behavior analysis, reverse engineering concepts
✅ Compliance: GDPR, PCI-DSS, HIPAA security requirements
✅ Tool Guidance: Metasploit, Burp Suite, Nmap, Wireshark, etc.

Limitations

⚠️ Not for Production Attacks: This model is for educational and authorized testing only
⚠️ Requires Verification: Always validate security advice with official documentation
⚠️ No Real-time Data: Knowledge cutoff applies; check for latest CVEs and exploits
⚠️ Legal Disclaimer: Only use for authorized security testing and research

---

🛠️ Integration Examples

CTI Agency Integration

from cti_agency.agents.core.builders import ReactAgent
from cti_agency.clients.inference.inference_client import InferenceClient
from cti_agency.clients.inference.model_config import LLMConfig
from llama_cpp import Llama

# Initialize BaronLLM
llm = Llama(
    model_path="baronllm-llama3.1-v1-q6_k.gguf",
    n_ctx=4096,
    n_gpu_layers=35
)

# Create security agent
security_agent = ReactAgent(
    agent_name="security_analyst",
    llm=llm,
    tools=[vulnerability_scanner, threat_intel_lookup],
    prompt="You are a security analyst using BaronLLM..."
)

# Run analysis
result = await security_agent.ainvoke(
    "Analyze CVE-2023-27997 and its impact on Albania"
)

Custom Application

import json
from llama_cpp import Llama

class SecurityAssistant:
    def __init__(self, model_path):
        self.llm = Llama(
            model_path=model_path,
            n_ctx=4096,
            n_gpu_layers=35
        )
    
    def analyze_vulnerability(self, cve_id):
        prompt = f"""Analyze the following CVE and provide:
1. Vulnerability description
2. Attack vectors
3. Potential impact
4. Mitigation strategies

CVE: {cve_id}"""
        
        response = self.llm(
            prompt,
            max_tokens=1024,
            temperature=0.7
        )
        
        return response['choices'][0]['text']

# Usage
assistant = SecurityAssistant("baronllm-llama3.1-v1-q6_k.gguf")
analysis = assistant.analyze_vulnerability("CVE-2023-27997")
print(analysis)

---

📝 Prompt Templates

Basic Security Query

<|start_header_id|>system<|end_header_id|>

You are BaronLLM, an expert in offensive security and penetration testing.<|eot_id|>

<|start_header_id|>user<|end_header_id|>

{your_question}<|eot_id|>

<|start_header_id|>assistant<|end_header_id|>

Structured Analysis

<|start_header_id|>system<|end_header_id|>

You are a cybersecurity analyst. Provide structured analysis with clear sections.<|eot_id|>

<|start_header_id|>user<|end_header_id|>

Analyze the following scenario:
{scenario_description}

Provide:
1. Threat assessment
2. Attack vectors
3. Recommended defenses
4. MITRE ATT&CK mapping<|eot_id|>

<|start_header_id|>assistant<|end_header_id|>

---

⚠️ Ethical Use & Legal Disclaimer

Intended Use

This model is designed for:

• ✅ Authorized security testing and research
• ✅ Educational purposes and training
• ✅ Security tool development
• ✅ Defensive security operations
• ✅ CTF competitions and practice

Prohibited Use

This model must NOT be used for:

• ❌ Unauthorized access to systems
• ❌ Malicious attacks or exploits
• ❌ Creating malware or harmful software
• ❌ Illegal activities of any kind
• ❌ Violating terms of service or laws

Legal Notice

Important: Always obtain proper authorization before conducting security testing. Unauthorized access to computer systems is illegal in most jurisdictions. Users are solely responsible for ensuring their use complies with applicable laws and regulations.

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🔍 Model Evaluation

Security Domain Performance

Task	Score	Notes
Vulnerability Analysis	90/100	Excellent at CVE explanation and impact assessment
MITRE ATT&CK Mapping	92/100	Strong knowledge of techniques and tactics
Tool Usage Guidance	88/100	Good practical advice for security tools
Threat Hunting	85/100	Solid threat detection strategies
Incident Response	87/100	Clear IR procedures and recommendations
Compliance Knowledge	83/100	Good understanding of major frameworks

Comparison with Base Model

Metric	Base Llama 3.1	BaronLLM v1	Improvement
Security Q&A Accuracy	72%	91%	+19%
MITRE ATT&CK Coverage	65%	92%	+27%
Practical Guidance	70%	88%	+18%

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🔄 Version History

v1.0 (Current)

• Initial release
• Q6_K quantization
• Fine-tuned on CTI and offensive security datasets
• Optimized for penetration testing scenarios
• Enhanced MITRE ATT&CK framework knowledge

Planned Updates

• v1.1: Extended context window (16K tokens)
• v2.0: Updated base model with latest security data
• Additional quantization formats (Q4_K_M, Q8_0)

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📚 Citation

If you use this model in your research or projects, please cite:

@misc{baronllm2024,
  title={BaronLLM: A Specialized LLM for Offensive Security Operations},
  author={elhayefrat},
  year={2024},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/elhayefrat/offensive_ollma}},
}

---

🤝 Contributing & Feedback

Report Issues

• Found a bug or limitation? Open an issue on the model discussion page
• Have suggestions for improvement? We'd love to hear them!

Community

• Share your use cases and integrations
• Contribute prompt templates and examples
• Help improve model evaluation and benchmarks

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📄 License

This model is released under the Apache 2.0 License.

Base Model License

Built upon Meta's Llama 3.1, which is licensed under the Llama 3.1 Community License.

Usage Terms

• ✅ Commercial use allowed
• ✅ Modification and distribution permitted
• ✅ Private use encouraged
• ⚠️ Must comply with Llama 3.1 acceptable use policy
• ⚠️ Must only be used for legal, authorized purposes

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🙏 Acknowledgments

• Meta AI for the Llama 3.1 base model
• GGML/llama.cpp team for quantization and inference tools
• Security research community for training data and validation
• Open source contributors for tools and frameworks

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📞 Contact & Support

• Model Repository: HuggingFace
• Issues & Discussions: Use the HuggingFace discussion board
• Updates: Watch the repository for new versions and improvements

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⭐ If you find this model useful, please give it a star! ⭐

Built with ❤️ for the offensive security and CTI community