FKIE_CVE-2025-59432
Vulnerability from fkie_nvd - Published: 2025-09-22 20:15 - Updated: 2026-10-11 20:10
Severity
Summary
SCRAM (Salted Challenge Response Authentication Mechanism) is part of the family of Simple Authentication and Security Layer (SASL, RFC 4422) authentication mechanisms. Prior to version 3.2, a timing attack vulnerability exists in the SCRAM Java implementation. The issue arises because Arrays.equals was used to compare secret values such as client proofs and server signatures. Since Arrays.equals performs a short-circuit comparison, the execution time varies depending on how many leading bytes match. This behavior could allow an attacker to perform a timing side-channel attack and potentially infer sensitive authentication material. All users relying on SCRAM authentication are impacted. This vulnerability has been patched in version 3.1 by replacing Arrays.equals with MessageDigest.isEqual, which ensures constant-time comparison.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "scram",
"vendor": "ongres",
"versions": [
{
"status": "affected",
"version": "\u003c 3.2"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "SCRAM (Salted Challenge Response Authentication Mechanism) is part of the family of Simple Authentication and Security Layer (SASL, RFC 4422) authentication mechanisms. Prior to version 3.2, a timing attack vulnerability exists in the SCRAM Java implementation. The issue arises because Arrays.equals was used to compare secret values such as client proofs and server signatures. Since Arrays.equals performs a short-circuit comparison, the execution time varies depending on how many leading bytes match. This behavior could allow an attacker to perform a timing side-channel attack and potentially infer sensitive authentication material. All users relying on SCRAM authentication are impacted. This vulnerability has been patched in version 3.1 by replacing Arrays.equals with MessageDigest.isEqual, which ensures constant-time comparison."
},
{
"lang": "es",
"value": "SCRAM (Mecanismo de Autenticaci\u00f3n de Respuesta a Desaf\u00edo Salado) forma parte de la familia de mecanismos de autenticaci\u00f3n de la Capa de Autenticaci\u00f3n y Seguridad Simple (SASL, RFC 4422). Antes de la versi\u00f3n 3.2, existe una vulnerabilidad de ataque de temporizaci\u00f3n en la implementaci\u00f3n Java de SCRAM. El problema surge porque se utiliz\u00f3 Arrays.equals para comparar valores secretos como pruebas del cliente y firmas del servidor. Dado que Arrays.equals realiza una comparaci\u00f3n de cortocircuito, el tiempo de ejecuci\u00f3n var\u00eda dependiendo de cu\u00e1ntos bytes iniciales coincidan. Este comportamiento podr\u00eda permitir a un atacante realizar un ataque de canal lateral de temporizaci\u00f3n y potencialmente inferir material de autenticaci\u00f3n sensible. Todos los usuarios que dependen de la autenticaci\u00f3n SCRAM se ven afectados. Esta vulnerabilidad ha sido parcheada en la versi\u00f3n 3.1 al reemplazar Arrays.equals con MessageDigest.isEqual, lo que asegura una comparaci\u00f3n de tiempo constante."
}
],
"id": "CVE-2025-59432",
"lastModified": "2026-10-11T20:10:00.200",
"metrics": {
"cvssMetricV40": [
{
"cvssData": {
"Automatable": "NOT_DEFINED",
"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "LOW",
"attackRequirements": "NONE",
"attackVector": "NETWORK",
"availabilityRequirement": "NOT_DEFINED",
"baseScore": 6.6,
"baseSeverity": "MEDIUM",
"confidentialityRequirement": "NOT_DEFINED",
"exploitMaturity": "UNREPORTED",
"integrityRequirement": "NOT_DEFINED",
"modifiedAttackComplexity": "NOT_DEFINED",
"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
"modifiedSubAvailabilityImpact": "NOT_DEFINED",
"modifiedSubConfidentialityImpact": "NOT_DEFINED",
"modifiedSubIntegrityImpact": "NOT_DEFINED",
"modifiedUserInteraction": "NOT_DEFINED",
"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
"modifiedVulnConfidentialityImpact": "NOT_DEFINED",
"modifiedVulnIntegrityImpact": "NOT_DEFINED",
"privilegesRequired": "NONE",
"providerUrgency": "NOT_DEFINED",
"subAvailabilityImpact": "NONE",
"subConfidentialityImpact": "NONE",
"subIntegrityImpact": "NONE",
"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N/E:U/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"version": "4.0",
"vulnAvailabilityImpact": "NONE",
"vulnConfidentialityImpact": "HIGH",
"vulnIntegrityImpact": "NONE",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2025-59432",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-09-22T19:39:54.878581Z",
"version": "2.0.3"
}
}
]
},
"published": "2025-09-22T20:15:38.673",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://docs.oracle.com/en/java/javase/25/docs/api/java.base/java/security/MessageDigest.html#isEqual(byte%5B%5D,byte%5B%5D)"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/ongres/scram/commit/e0b0cf99f05406a0d26682c72fcb5728e95124b3"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/ongres/scram/security/advisories/GHSA-3wfh-36rx-9537"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-208"
},
{
"lang": "en",
"value": "CWE-385"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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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