GSD-2023-25563
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
GSS-NTLMSSP is a mechglue plugin for the GSSAPI library that implements NTLM authentication. Prior to version 1.2.0, multiple out-of-bounds reads when decoding NTLM fields can trigger a denial of service. A 32-bit integer overflow condition can lead to incorrect checks of consistency of length of internal buffers. Although most applications will error out before accepting a singe input buffer of 4GB in length this could theoretically happen. This vulnerability can be triggered via the main `gss_accept_sec_context` entry point if the application allows tokens greater than 4GB in length. This can lead to a large, up to 65KB, out-of-bounds read which could cause a denial-of-service if it reads from unmapped memory. Version 1.2.0 contains a patch for the out-of-bounds reads.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2023-25563",
"id": "GSD-2023-25563",
"references": [
"https://www.suse.com/security/cve/CVE-2023-25563.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
"details": "GSS-NTLMSSP is a mechglue plugin for the GSSAPI library that implements NTLM authentication. Prior to version 1.2.0, multiple out-of-bounds reads when decoding NTLM fields can trigger a denial of service. A 32-bit integer overflow condition can lead to incorrect checks of consistency of length of internal buffers. Although most applications will error out before accepting a singe input buffer of 4GB in length this could theoretically happen. This vulnerability can be triggered via the main `gss_accept_sec_context` entry point if the application allows tokens greater than 4GB in length. This can lead to a large, up to 65KB, out-of-bounds read which could cause a denial-of-service if it reads from unmapped memory. Version 1.2.0 contains a patch for the out-of-bounds reads.",
"id": "GSD-2023-25563",
"modified": "2023-12-13T01:20:40.882303Z",
"schema_version": "1.4.0"
}
},
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"affects": {
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"product": {
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"product_name": "gss-ntlmssp",
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}
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"impact": {
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"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 5.9,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
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{
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"url": "https://github.com/gssapi/gss-ntlmssp/releases/tag/v1.2.0"
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"source": {
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"discovery": "UNKNOWN"
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"value": "GSS-NTLMSSP is a mechglue plugin for the GSSAPI library that implements NTLM authentication. Prior to version 1.2.0, multiple out-of-bounds reads when decoding NTLM fields can trigger a denial of service. A 32-bit integer overflow condition can lead to incorrect checks of consistency of length of internal buffers. Although most applications will error out before accepting a singe input buffer of 4GB in length this could theoretically happen. This vulnerability can be triggered via the main `gss_accept_sec_context` entry point if the application allows tokens greater than 4GB in length. This can lead to a large, up to 65KB, out-of-bounds read which could cause a denial-of-service if it reads from unmapped memory. Version 1.2.0 contains a patch for the out-of-bounds reads."
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"impact": {
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"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
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"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
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"exploitabilityScore": 3.9,
"impactScore": 3.6
}
},
"lastModifiedDate": "2023-02-22T18:37Z",
"publishedDate": "2023-02-14T18:15Z"
}
}
}
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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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