Llama-3.1-Scan-Expert

Llama-expert-Scanner

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Model description

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language:

• en license: llama3.1 base_model: meta-llama/Llama-3.1-8B-Instruct tags:
• cybersecurity
• vulnerability-scanning
• nvd
• fine-tuned
• rag model_type: llama pipeline_tag: text-generation

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Llama-3.1-expert-Scanner-8b

This model is a fine-tuned version of Llama-3.1-8B-Instruct, specialized for cybersecurity vulnerability analysis and business impact assessment. It was developed to bridge the gap between technical scan results and executive risk management.

Model Description

Llama-3.1-Expert-Cyber is designed to act as a reasoning engine for security scanners. Unlike general-purpose models, it has been fine-tuned on a curated dataset of:

• Network scan outputs (Nmap, service banners).
• CVE/CWE technical descriptions.
• Professional remediation playbooks.
• Business asset criticality mappings.

Key Capabilities:

• Technical Reasoning: Translates raw port/service data into specific vulnerability identifications.
• RAG-Ready: Optimized to ingest context from vector databases (like Qdrant) to provide grounded, business-specific impact analysis.
• Remediation Strategy: Generates step-by-step technical fixes and defensive best practices.

Intended Use

• Security Analysts: For automated alert triage and summarization.
• Developers: To understand the security implications of their infrastructure.
• Freelancers/Consultants: To generate professional-grade security reports from raw scan data.

Training Data

The model was fine-tuned using a custom-built dataset

1. Instruction: "Analyze this scan result for {Service} on {Port}."
2. Context: NVD technical data and business asset descriptions.
3. Output: Comprehensive analysis including Severity, Business Impact, and Remediation.

Technical Stack

• Base Model: Meta Llama 3.1 8B Instruct
• Fine-Tuning Method: QLoRA (Parameter Efficient Fine-Tuning)
• Infrastructure: Trained and hosted on Google Cloud Platform (GCP).
• Inference: Optimized for local or cloud deployment (GGUF version available).

Limitations & Ethics

• Knowledge Cutoff: The model's internal knowledge is based on the training data. For the latest CVEs, it should be used in a RAG pipeline with a live NVD connection.
• No Autonomous Action: This model is an assistant. It should never be used to execute autonomous network changes without human oversight.
• Ethical Use: This tool is intended for defensive security and educational purposes only.

Trigger words

You should use cybersecurity to trigger the image generation.

You should use RAG to trigger the image generation.

You should use finetuned to trigger the image generation.

You should use Llama-3.1 to trigger the image generation.

You should use security-expert. to trigger the image generation.

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