FKIE_CVE-2026-103001
Vulnerability from fkie_nvd - Published: 2026-09-30 22:16 - Updated: 2026-09-30 22:16
Severity
Summary
PyJWT is a Python implementation of JSON Web Token standards. From 2.11.0 through 2.13.0, PyJWT's PyJWT._merge_options() method can modify a caller-supplied mutable options mapping when verify_signature is false. If an application reuses that same mapping for a later decode() or decode_complete() call and changes verify_signature to true, the mapping can retain false values for expiration, not-before, issued-at, audience, issuer, subject, and JWT ID checks. A signed token with invalid registered claims can then be accepted without disabling signature verification, but applications that create a fresh options mapping for each call are not affected.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "pyjwt",
"vendor": "jpadilla",
"versions": [
{
"status": "affected",
"version": "\u003e= 2.11.0, \u003c= 2.13.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "PyJWT is a Python implementation of JSON Web Token standards. From 2.11.0 through 2.13.0, PyJWT\u0027s PyJWT._merge_options() method can modify a caller-supplied mutable options mapping when verify_signature is false. If an application reuses that same mapping for a later decode() or decode_complete() call and changes verify_signature to true, the mapping can retain false values for expiration, not-before, issued-at, audience, issuer, subject, and JWT ID checks. A signed token with invalid registered claims can then be accepted without disabling signature verification, but applications that create a fresh options mapping for each call are not affected."
}
],
"id": "CVE-2026-103001",
"lastModified": "2026-09-30T22:16:33.537",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.2,
"impactScore": 4.2,
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
},
"published": "2026-09-30T22:16:33.537",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/jpadilla/pyjwt/commit/0c87c8c8b1a74cac99ad8115f3050efcb7fbed35"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/jpadilla/pyjwt/issues/679"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/jpadilla/pyjwt/security/advisories/GHSA-gvp8-978c-rx2q"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-471"
}
],
"source": "security-advisories@github.com",
"type": "Primary"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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