| | --- |
| | license: apache-2.0 |
| | task_categories: |
| | - text-generation |
| | language: |
| | - en |
| | tags: |
| | - code |
| | - vulnerability |
| | - cybersecurity |
| | - research |
| | pretty_name: CVE-LLm-Dataset |
| | data_source: Custom data collected from the CVE database |
| | data_formats: JSONL |
| | size_categories: |
| | - n<1K |
| | --- |
| | # CVE-llm_dataset |
| | This dataset is intended to train an LLM model for an utterly CVE-focused input and output. |
| | |
| | ## Data extraction: |
| | 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. |
| |
|
| | The CVE json files are mapped in this format: |
| | ``` |
| | cves: |
| | | |
| | ├─1999 |
| | | ├─0xxx |
| | | | ├─CVE-1999-0001.json |
| | | | ├─.... |
| | | | └─CVE-1999-0999.json |
| | | └─1xxx |
| | | ├─CVE-1999-1000.json |
| | | ├─.... |
| | | └─CVE-1999-1598.json |
| | └─2023 |
| | |
| | ``` |
| | The programs traverse through these folders extract the data in the files and arrange them into usable formats for the fine-tuning process. |
| |
|
| | ## llama2 and Openai Model dataset: |
| | The llama2 fine-tuned dataset follows this format: |
| | ```json |
| | { |
| | "instruction": "Explain CVE-1999-0001", |
| | "input": "Explain the vulnerability: CVE-1999-0001", |
| | "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" |
| | } |
| | ``` |
| |
|
| | The openai fine-tune dataset follows this format: |
| | ```json |
| | { |
| | "messages": [ |
| | {"role": "system", "content": "CVE Vulnerability Information"}, |
| | {"role": "user", "content": f"Explain the CVE ID: What does the identifier {cve_id} mean in the context of cybersecurity?"}, |
| | {"role": "assistant", "content": cve_id} |
| | ] |
| | }, |
| | { |
| | "messages": [ |
| | {"role": "system", "content": "CVE Vulnerability Information"}, |
| | {"role": "user", "content": f"Describe the vulnerability: Provide detailed information about the vulnerability for {cve_id}."}, |
| | {"role": "assistant", "content": description} |
| | ] |
| | }, |
| | { |
| | "messages": [ |
| | {"role": "system", "content": "CVE Vulnerability Information"}, |
| | {"role": "user", "content": f"Provide references for further information: Can you share references or external links related to {cve_id}?"}, |
| | {"role": "assistant", "content": references} |
| | ] |
| | }, |
| | { |
| | "messages": [ |
| | {"role": "system", "content": "CVE Vulnerability Information"}, |
| | {"role": "user", "content": f"Explain the state of this CVE: What is the publication or resolution state of {cve_id}?"}, |
| | {"role": "assistant", "content": cve_state} |
| | ] |
| | } |
| | ``` |
| |
|
| | 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. |
| |
|
| | 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. |
| |
|
| | 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. |
