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--- |
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license: apache-2.0 |
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task_categories: |
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- text-generation |
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language: |
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- en |
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tags: |
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- code |
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- vulnerability |
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- cybersecurity |
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- research |
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pretty_name: CVE-LLm-Dataset |
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data_source: Custom data collected from the CVE database |
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data_formats: JSONL |
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size_categories: |
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- n<1K |
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--- |
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# CVE-llm_dataset |
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This dataset is intended to train an LLM model for an utterly CVE-focused input and output. |
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## Data extraction: |
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For the data extraction, I first downloaded the CVE database from NVD lists and then loaded them using the `cve_dataset_2.py` and `cve_dataset.py` both have produce different datasets one is for llama and the other is for openai GPT. |
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The CVE json files are mapped in this format: |
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``` |
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cves: |
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├─1999 |
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| ├─0xxx |
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| | ├─CVE-1999-0001.json |
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| | ├─.... |
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| | └─CVE-1999-0999.json |
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| └─1xxx |
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| ├─CVE-1999-1000.json |
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| ├─.... |
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| └─CVE-1999-1598.json |
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└─2023 |
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``` |
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The programs traverse through these folders extract the data in the files and arrange them into usable formats for the fine-tuning process. |
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## llama2 and Openai Model dataset: |
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The llama2 fine-tuned dataset follows this format: |
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```json |
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{ |
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"instruction": "Explain CVE-1999-0001", |
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"input": "Explain the vulnerability: CVE-1999-0001", |
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"output": "ip_input.c in BSD-derived TCP/IP implementations allows remote attackers to cause a denial of service (crash or hang) via crafted packets.\nAffected Products: n/a\nReferences: [{'tags': ['x_refsource_CONFIRM'], 'url': 'http://www.openbsd.org/errata23.html#tcpfix'}, {'name': '5707', 'tags': ['vdb-entry', 'x_refsource_OSVDB'], 'url': 'http://www.osvdb.org/5707'}]\nCVE State: PUBLISHED" |
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} |
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``` |
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The openai fine-tune dataset follows this format: |
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```json |
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{ |
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"messages": [ |
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{"role": "system", "content": "CVE Vulnerability Information"}, |
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{"role": "user", "content": f"Explain the CVE ID: What does the identifier {cve_id} mean in the context of cybersecurity?"}, |
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{"role": "assistant", "content": cve_id} |
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] |
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}, |
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{ |
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"messages": [ |
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{"role": "system", "content": "CVE Vulnerability Information"}, |
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{"role": "user", "content": f"Describe the vulnerability: Provide detailed information about the vulnerability for {cve_id}."}, |
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{"role": "assistant", "content": description} |
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] |
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}, |
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{ |
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"messages": [ |
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{"role": "system", "content": "CVE Vulnerability Information"}, |
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{"role": "user", "content": f"Provide references for further information: Can you share references or external links related to {cve_id}?"}, |
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{"role": "assistant", "content": references} |
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] |
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}, |
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{ |
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"messages": [ |
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{"role": "system", "content": "CVE Vulnerability Information"}, |
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{"role": "user", "content": f"Explain the state of this CVE: What is the publication or resolution state of {cve_id}?"}, |
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{"role": "assistant", "content": cve_state} |
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] |
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} |
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``` |
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The instruction is what we instruct the AI to do with the data provided For example we can command the AI `To take in user input analyze it and then based on what he asks returns an answer` This is also where we can add a `role` or a `personal` to the AI. |
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The input is the user Input of the main query or data that must be processed by the AI. This is a crucial piece of information that the AI will process in order to provide an output. |
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The output is the format that we define and tell the AI to generate answers in that format or provide that answer to the question asked. |
