FKIE_CVE-2026-57178
Vulnerability from fkie_nvd - Published: 2026-09-24 18:17 - Updated: 2026-09-25 13:27
Severity
Summary
Python Social Auth is a social authentication/registration mechanism. Prior to version 5.0.0, the `vk-app` backend accepted VK application callback data without verifying the callback signature when the `auth_key` parameter was omitted. Applications using this backend could treat unsigned attacker-controlled data as a verified VK identity. An attacker could choose callback fields such as `viewer_id`, `access_token`, `api_id`, and `api_result`, potentially allowing authentication as an arbitrary VK user ID. The issue affects only applications using the `vk-app` backend. The issue has been fixed in version 5.0.0 by requiring `auth_key` to be present and valid before callback data is trusted.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "social-core",
"vendor": "python-social-auth",
"versions": [
{
"status": "affected",
"version": "\u003c 5.0.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Python Social Auth is a social authentication/registration mechanism. Prior to version 5.0.0, the `vk-app` backend accepted VK application callback data without verifying the callback signature when the `auth_key` parameter was omitted. Applications using this backend could treat unsigned attacker-controlled data as a verified VK identity. An attacker could choose callback fields such as `viewer_id`, `access_token`, `api_id`, and `api_result`, potentially allowing authentication as an arbitrary VK user ID. The issue affects only applications using the `vk-app` backend. The issue has been fixed in version 5.0.0 by requiring `auth_key` to be present and valid before callback data is trusted."
}
],
"id": "CVE-2026-57178",
"lastModified": "2026-09-25T13:27:31.790",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.4,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.2,
"impactScore": 5.2,
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
},
"published": "2026-09-24T18:17:14.970",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/python-social-auth/social-core/security/advisories/GHSA-3c93-f73f-qc9h"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Awaiting Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-287"
},
{
"lang": "en",
"value": "CWE-347"
}
],
"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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