Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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vulnerability-scores

This dataset comprises 731,020 real-world vulnerabilities used to train and evaluate VLAI, a transformer-based model designed to predict software vulnerability severity levels directly from text descriptions, enabling faster and more consistent triage.

The dataset is presented in the paper VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification.

Sources

Source	Label	Entries	Share
`cvelistv5`	CVE Program (enriched with vulnrichment and Fraunhofer FKIE)	345,760	47.3%
`github`	GitHub Security Advisories	345,052	47.2%
`csaf_redhat`	CSAF Red Hat	26,218	3.6%
`csaf_cisa`	CSAF CISA	5,944	0.8%
`pysec`	PySec advisories	4,105	0.6%
`csaf_cisco`	CSAF Cisco	3,941	0.5%

Extracted from the database of Vulnerability-Lookup with the VulnTrain project. Dumps of the data are available here.

Splits

Split	Examples
train	657,918
test	73,102

Fields

Field	Type	Description
`id`	string	Vulnerability identifier (e.g., CVE-2024-1234, GHSA-xxxx, PYSEC-2024-xxx)
`title`	string	Vulnerability title
`description`	string	Vulnerability description in English
`cpes`	list[string]	Common Platform Enumeration identifiers
`cvss_v4_0`	float	CVSS v4.0 score
`cvss_v3_1`	float	CVSS v3.1 score
`cvss_v3_0`	float	CVSS v3.0 score
`cvss_v2_0`	float	CVSS v2.0 score
`patch_commit_url`	string	URL to the patch commit on GitHub, if available
`source`	string	Data source identifier

Usage

import json
from datasets import load_dataset

dataset = load_dataset("CIRCL/vulnerability-scores")

vulnerabilities = ["CVE-2012-2339", "RHSA-2023:5964", "GHSA-7chm-34j8-4f22", "PYSEC-2024-225"]

filtered_entries = dataset.filter(lambda elem: elem["id"] in vulnerabilities)

for entry in filtered_entries["train"]:
    print(json.dumps(entry, indent=4))