CVE-2023-25566 (GCVE-0-2023-25566)
Vulnerability from cvelistv5 – Published: 2023-02-14 17:35 – Updated: 2025-03-10 21:11
VLAI
EPSS
VEX
Title
GSS-NTLMSSP vulnerable to memory leak when parsing usernames
Summary
GSS-NTLMSSP is a mechglue plugin for the GSSAPI library that implements NTLM authentication. Prior to version 1.2.0, a memory leak can be triggered when parsing usernames which can trigger a denial-of-service. The domain portion of a username may be overridden causing an allocated memory area the size of the domain name to be leaked. An attacker can leak memory via the main `gss_accept_sec_context` entry point, potentially causing a denial-of-service. This issue is fixed in version 1.2.0.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-03-10 20:58 UTC
CWE
- CWE-401 - Missing Release of Memory after Effective Lifetime
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/gssapi/gss-ntlmssp/security/ad… | x_refsource_CONFIRM |
| https://github.com/gssapi/gss-ntlmssp/commit/8660… | x_refsource_MISC |
| https://github.com/gssapi/gss-ntlmssp/releases/ta… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| gssapi | gss-ntlmssp |
Affected:
< 1.2.0
|
guessed |
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},
{
"name": "https://github.com/gssapi/gss-ntlmssp/releases/tag/v1.2.0",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/gssapi/gss-ntlmssp/releases/tag/v1.2.0"
}
],
"source": {
"advisory": "GHSA-mfm4-6g58-jw74",
"discovery": "UNKNOWN"
},
"title": "GSS-NTLMSSP vulnerable to memory leak when parsing usernames"
}
},
"cveMetadata": {
"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"assignerShortName": "GitHub_M",
"cveId": "CVE-2023-25566",
"datePublished": "2023-02-14T17:35:48.482Z",
"dateReserved": "2023-02-07T17:10:00.737Z",
"dateUpdated": "2025-03-10T21:11:49.420Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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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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