misc 0 0 0

7h3-R3v3n4n7/pentest-agent-dataset-chatml

published by @7h3-R3v3n4n7

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
3K
Last 30 days
247
Likes
9
Size
321.8 MB
Created on HF
2025-06-05
Age
1 years ago

Description

Pentest Agent Dataset - ChatML

This dataset is part of the Pentest Agent project and contains cybersecurity data formatted for fine-tuning language models.

Data Sources

Based on real CVEs from MITRE/NVD

Enriched with CVSS impact metrics

Linked to exploit code when available

Includes real-world pentesting scenarios

Contains command logic and execution steps

Includes red team techniques with MITRE references

Generated in ChatML format

Structure

Each… See the full description on the dataset page: https://huggingface.co/datasets/7h3-R3v3n4n7/pentest-agent-dataset-chatml.

Tags

language:enlicense:apache-2.0size_categories:100K<n<1Mformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:uscybersecuritypentestingvulnerabilityCVEexploit

README current version from Hugging Face


license: apache-2.0
datasets:

  • cve
  • exploitdb
  • mitre
  • nvd
  • pentesting-scenarios
  • command-logic
  • redteam-steering
    language:
  • en
    tags:
  • cybersecurity
  • pentesting
  • vulnerability
  • CVE
  • exploit
    pretty_name: Pentest Agent Dataset - ChatML

Pentest Agent Dataset - ChatML

This dataset is part of the Pentest Agent project and contains cybersecurity data formatted for fine-tuning language models.

Data Sources

  • Based on real CVEs from MITRE/NVD
  • Enriched with CVSS impact metrics
  • Linked to exploit code when available
  • Includes real-world pentesting scenarios
  • Contains command logic and execution steps
  • Includes red team techniques with MITRE references
  • Generated in ChatML format

Structure

Each sample contains:

  • Instruction: A prompt requesting information about a CVE or pentesting task
  • Output: A detailed explanation including impact data, exploit (if found), and reference links.

CVE Example

{
    "messages": [
        {
            "role": "user",
            "content": "Can you explain what CVE-2023-1234 is?"
        },
        {
            "role": "assistant",
            "content": "CVE-2023-1234 is a critical vulnerability in Example Software that allows remote code execution..."
        }
    ]
}

Pentesting Scenario Example

{
    "messages": [
        {
            "role": "user",
            "content": "Perform a penetration test with the following goal: Gain access to the internal network"
        },
        {
            "role": "assistant",
            "content": "Initial Context: The target is a corporate network with a public-facing web server...\n\nThinking Process: First, we need to identify potential entry points...\n\nReconnaissance: Begin with port scanning and service enumeration..."
        }
    ]
}

Command Logic Example

{
    "messages": [
        {
            "role": "user",
            "content": "How can I perform a network scan to identify open ports?"
        },
        {
            "role": "assistant",
            "content": "1. Use nmap to scan the target network:\n   nmap -sV 192.168.1.0/24\n2. For a more detailed scan:\n   nmap -p- -sV -sC 192.168.1.0/24"
        }
    ]
}

Red Team Example

{
    "messages": [
        {
            "role": "user",
            "content": "How can we use SNMP enumeration to find sensitive information?"
        },
        {
            "role": "assistant",
            "content": "SNMP enumeration can be used to find sensitive information by querying the Management Information Base (MIB). Use tools like snmpwalk to query the SNMP service and extract information such as device serial numbers, running services, and network configurations.\n\nMITRE References:\nMITRE ID: T1046\nMITRE Name: Network Service Scanning\nMITRE Tactics: Discovery"
        }
    ]
}

Sources

Vulnerability Data

Pentesting & Red Team Data


Generated by the Pentest Agent Dataset Pipeline.

README history 10 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2025-06-11Upload dataset.jsonl with huggingface_hub835a9493.4 KB
    Loading...
  2. 2025-06-11Upload README.md with huggingface_hubba778923.4 KB
    Loading...
  3. 2025-06-11Upload dataset.jsonl with huggingface_hubd1c17f63.4 KB
    Loading...
  4. 2025-06-07Upload dataset.jsonl with huggingface_hub43861a53.4 KB
    Loading...
  5. 2025-06-06Upload dataset.jsonl with huggingface_hub5ccba043.4 KB
    Loading...
  6. 2025-06-06Upload README.md with huggingface_hub89b9e063.4 KB
    Loading...
  7. 2025-06-06Upload dataset.jsonl with huggingface_hub8f619322.7 KB
    Loading...
  8. 2025-06-06Create README.mddb7e0842.7 KB
    Loading...
  9. 2025-06-05Upload dataset.jsonl with huggingface_hubf4a2cdd0 B
    Loading...
  10. 2025-06-05initial commitc5753520 B
    Loading...

Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2025-06-06[bot] Conversion to Parquetopen1 💬#1
    Loading...

Related datasets same stage · misc

Read further

Metadata

License
apache-2.0

Repository

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("7h3-R3v3n4n7/pentest-agent-dataset-chatml")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="7h3-R3v3n4n7/pentest-agent-dataset-chatml", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/7h3-R3v3n4n7/pentest-agent-dataset-chatml
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration