| Name | VulnTrain JSON |
| Version |
0.5.1
JSON |
| download |
| home_page | None |
| Summary | Generate datasets amd models based on vulnerabilities descriptions from Vulnerability-Lookup. |
| upload_time | 2025-02-21 23:02:42 |
| maintainer | None |
| docs_url | None |
| author | Cédric Bonhomme |
| requires_python | <4.0,>=3.10 |
| license | GPL-3.0-or-later |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# VulnTrain
Generate datasets amd models based on vulnerabilities descriptions from
[Vulnerability-Lookup](https://github.com/vulnerability-lookup/vulnerability-lookup).
Uses data from the ``vulnerability-lookup:meta`` container such as vulnrichment and FKIE.
## Datasets
Various datasets generated are available on HuggingFace:
https://huggingface.co/datasets/circl/vulnerability
## Usage
### Generate datasets
Authenticate to HuggingFace:
```bash
huggingface-cli login
```
Install VulnTrain:
```bash
$ pipx install VulnTrain
```
Then ensures that the kvrocks database of Vulnerability-Lookup is running.
Creation of datasets:
```bash
$ vulntrain-create-dataset --nb-rows 10000 --upload --repo-id CIRCL/vulnerability-dataset-10k
Generating train split: 9999 examples [00:00, 177710.74 examples/s]
DatasetDict({
train: Dataset({
features: ['id', 'title', 'description', 'cpes'],
num_rows: 8999
})
test: Dataset({
features: ['id', 'title', 'description', 'cpes'],
num_rows: 1000
})
})
Creating parquet from Arrow format: 100%|██████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 49.66ba/s]
Uploading the dataset shards: 100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.03s/it]
Creating parquet from Arrow format: 100%|██████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 63.36ba/s]
Uploading the dataset shards: 100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.19s/it]
README.md: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 503/503 [00:00<00:00, 2.34MB/s]
```
### Train
#### Training for text generation
For now we are using distilbert-base-uncased (AutoModelForMaskedLM) or gpt2 (AutoModelForCausalLM).
The goal is to generate text.
```bash
$ vulntrain-train-dataset --base-model gpt2 --model-name CIRCL/vulnerability
Using CUDA (Nvidia GPU).
[codecarbon WARNING @ 13:28:13] Multiple instances of codecarbon are allowed to run at the same time.
[codecarbon INFO @ 13:28:13] [setup] RAM Tracking...
[codecarbon INFO @ 13:28:13] [setup] CPU Tracking...
[codecarbon WARNING @ 13:28:13] No CPU tracking mode found. Falling back on CPU constant mode.
Linux OS detected: Please ensure RAPL files exist at \sys\class\powercap\intel-rapl to measure CPU
[codecarbon WARNING @ 13:28:14] We saw that you have a AMD EPYC 9124 16-Core Processor but we don't know it. Please contact us.
[codecarbon INFO @ 13:28:14] CPU Model on constant consumption mode: AMD EPYC 9124 16-Core Processor
[codecarbon INFO @ 13:28:14] [setup] GPU Tracking...
[codecarbon INFO @ 13:28:14] Tracking Nvidia GPU via pynvml
[codecarbon INFO @ 13:28:14] >>> Tracker's metadata:
[codecarbon INFO @ 13:28:14] Platform system: Linux-6.8.0-48-generic-x86_64-with-glibc2.39
[codecarbon INFO @ 13:28:14] Python version: 3.12.3
[codecarbon INFO @ 13:28:14] CodeCarbon version: 2.8.3
[codecarbon INFO @ 13:28:14] Available RAM : 251.586 GB
[codecarbon INFO @ 13:28:14] CPU count: 64
[codecarbon INFO @ 13:28:14] CPU model: AMD EPYC 9124 16-Core Processor
[codecarbon INFO @ 13:28:14] GPU count: 2
[codecarbon INFO @ 13:28:14] GPU model: 2 x NVIDIA L40S
[codecarbon INFO @ 13:28:18] Saving emissions data to file /home/cedric/VulnTrain/emissions.csv | 1/2700 [00:07<5:45:36, 7.68s/it]
...
...
...
```
#### Training for classification
tf-idf on the vulnerability descriptions.
### Validation
It is possible to send prompts to a model trained for text generation (descriptions of vulnerabilities).
```bash
$ vulntrain-validate-text-generation --help
usage: vulntrain-validate-text-generation [-h] [--model MODEL] [--prompt PROMPT]
Validate a text generation model for vulnerabilities.
options:
-h, --help show this help message and exit
--model MODEL The model to use.
--prompt PROMPT The prompt for the generator.
```
Example:
```bash
$ vulntrain-validate-text-generation --prompt "A new vulnerability in OpenSSL allows attackers to" --model CIRCL/vulnerability
config.json: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 907/907 [00:00<00:00, 6.70MB/s]
model.safetensors: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 498M/498M [00:12<00:00, 41.3MB/s]
generation_config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 119/119 [00:00<00:00, 1.63MB/s]
tokenizer_config.json: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 556/556 [00:00<00:00, 4.01MB/s]
vocab.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 798k/798k [00:00<00:00, 3.25MB/s]
merges.txt: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 456k/456k [00:00<00:00, 5.58MB/s]
tokenizer.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.56M/3.56M [00:00<00:00, 10.3MB/s]
special_tokens_map.json: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 470/470 [00:00<00:00, 3.51MB/s]
Device set to use cuda:0
Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
[{'generated_text': 'A new vulnerability in OpenSSL allows attackers to cause a Denial of Service (DoS) when receiving a specially crafted SIP message.\n\n\nThis issue affects: OpenSSL versions prior to 1.2.1\n\n\n\n * OpenSSL 1.2.1 prior to 1.2.1-HF1, which fixes this issue.\n\n * OpenSSL version 1.2.1 prior to 1.2.1-HF1 and OpenSSL 1.2.2 prior'}]
```
## License
[VulnTrain](https://github.com/vulnerability-lookup/VulnTrain) is licensed under
[GNU General Public License version 3](https://www.gnu.org/licenses/gpl-3.0.html)
~~~
Copyright (c) 2025 Computer Incident Response Center Luxembourg (CIRCL)
Copyright (C) 2025 Cédric Bonhomme - https://github.com/cedricbonhomme
~~~
Raw data
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"author": "C\u00e9dric Bonhomme",
"author_email": "cedric.bonhomme@circl.lu",
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"description": "# VulnTrain\n\nGenerate datasets amd models based on vulnerabilities descriptions from\n[Vulnerability-Lookup](https://github.com/vulnerability-lookup/vulnerability-lookup).\n\nUses data from the ``vulnerability-lookup:meta`` container such as vulnrichment and FKIE.\n\n\n## Datasets\n\nVarious datasets generated are available on HuggingFace:\n\nhttps://huggingface.co/datasets/circl/vulnerability\n\n\n## Usage\n\n### Generate datasets\n\nAuthenticate to HuggingFace:\n\n```bash\nhuggingface-cli login\n```\n\nInstall VulnTrain:\n\n```bash\n$ pipx install VulnTrain\n```\n\nThen ensures that the kvrocks database of Vulnerability-Lookup is running.\n\n\nCreation of datasets:\n\n```bash\n$ vulntrain-create-dataset --nb-rows 10000 --upload --repo-id CIRCL/vulnerability-dataset-10k\nGenerating train split: 9999 examples [00:00, 177710.74 examples/s]\nDatasetDict({\n train: Dataset({\n features: ['id', 'title', 'description', 'cpes'],\n num_rows: 8999\n })\n test: Dataset({\n features: ['id', 'title', 'description', 'cpes'],\n num_rows: 1000\n })\n})\nCreating parquet from Arrow format: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9/9 [00:00<00:00, 49.66ba/s]\nUploading the dataset shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:02<00:00, 2.03s/it]\nCreating parquet from Arrow format: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 63.36ba/s]\nUploading the dataset shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:01<00:00, 1.19s/it]\nREADME.md: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 503/503 [00:00<00:00, 2.34MB/s]\n```\n\n\n### Train\n\n#### Training for text generation\n\nFor now we are using distilbert-base-uncased (AutoModelForMaskedLM) or gpt2 (AutoModelForCausalLM).\nThe goal is to generate text.\n\n```bash\n$ vulntrain-train-dataset --base-model gpt2 --model-name CIRCL/vulnerability\nUsing CUDA (Nvidia GPU).\n[codecarbon WARNING @ 13:28:13] Multiple instances of codecarbon are allowed to run at the same time.\n[codecarbon INFO @ 13:28:13] [setup] RAM Tracking...\n[codecarbon INFO @ 13:28:13] [setup] CPU Tracking...\n[codecarbon WARNING @ 13:28:13] No CPU tracking mode found. Falling back on CPU constant mode. \n Linux OS detected: Please ensure RAPL files exist at \\sys\\class\\powercap\\intel-rapl to measure CPU\n\n[codecarbon WARNING @ 13:28:14] We saw that you have a AMD EPYC 9124 16-Core Processor but we don't know it. Please contact us.\n[codecarbon INFO @ 13:28:14] CPU Model on constant consumption mode: AMD EPYC 9124 16-Core Processor\n[codecarbon INFO @ 13:28:14] [setup] GPU Tracking...\n[codecarbon INFO @ 13:28:14] Tracking Nvidia GPU via pynvml\n[codecarbon INFO @ 13:28:14] >>> Tracker's metadata:\n[codecarbon INFO @ 13:28:14] Platform system: Linux-6.8.0-48-generic-x86_64-with-glibc2.39\n[codecarbon INFO @ 13:28:14] Python version: 3.12.3\n[codecarbon INFO @ 13:28:14] CodeCarbon version: 2.8.3\n[codecarbon INFO @ 13:28:14] Available RAM : 251.586 GB\n[codecarbon INFO @ 13:28:14] CPU count: 64\n[codecarbon INFO @ 13:28:14] CPU model: AMD EPYC 9124 16-Core Processor\n[codecarbon INFO @ 13:28:14] GPU count: 2\n[codecarbon INFO @ 13:28:14] GPU model: 2 x NVIDIA L40S\n[codecarbon INFO @ 13:28:18] Saving emissions data to file /home/cedric/VulnTrain/emissions.csv | 1/2700 [00:07<5:45:36, 7.68s/it]\n...\n...\n...\n```\n\n\n#### Training for classification\n\ntf-idf on the vulnerability descriptions.\n\n\n\n### Validation\n\nIt is possible to send prompts to a model trained for text generation (descriptions of vulnerabilities).\n\n```bash\n$ vulntrain-validate-text-generation --help\nusage: vulntrain-validate-text-generation [-h] [--model MODEL] [--prompt PROMPT]\n\nValidate a text generation model for vulnerabilities.\n\noptions:\n -h, --help show this help message and exit\n --model MODEL The model to use.\n --prompt PROMPT The prompt for the generator.\n```\n\nExample:\n\n```bash\n$ vulntrain-validate-text-generation --prompt \"A new vulnerability in OpenSSL allows attackers to\" --model CIRCL/vulnerability\nconfig.json: 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907/907 [00:00<00:00, 6.70MB/s]\nmodel.safetensors: 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498M/498M [00:12<00:00, 41.3MB/s]\ngeneration_config.json: 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798k/798k [00:00<00:00, 3.25MB/s]\nmerges.txt: 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456k/456k [00:00<00:00, 5.58MB/s]\ntokenizer.json: 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470/470 [00:00<00:00, 3.51MB/s]\nDevice set to use cuda:0\nTruncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n\n[{'generated_text': 'A new vulnerability in OpenSSL allows attackers to cause a Denial of Service (DoS) when receiving a specially crafted SIP message.\\n\\n\\nThis issue affects: OpenSSL versions prior to 1.2.1\\n\\n\\n\\n * OpenSSL 1.2.1 prior to 1.2.1-HF1, which fixes this issue.\\n\\n * OpenSSL version 1.2.1 prior to 1.2.1-HF1 and OpenSSL 1.2.2 prior'}]\n```\n\n\n\n## License\n\n[VulnTrain](https://github.com/vulnerability-lookup/VulnTrain) is licensed under\n[GNU General Public License version 3](https://www.gnu.org/licenses/gpl-3.0.html)\n\n~~~\nCopyright (c) 2025 Computer Incident Response Center Luxembourg (CIRCL)\nCopyright (C) 2025 C\u00e9dric Bonhomme - https://github.com/cedricbonhomme\n~~~\n\n\n",
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