CVE-2025-65100 (GCVE-0-2025-65100)
Vulnerability from cvelistv5 – Published: 2025-11-19 18:52 – Updated: 2025-11-19 20:28
VLAI
EPSS
VEX
Title
Security Snapshot May Use Unintended Timestamp When Only ISAR_APT_SNAPSHOT_DATE Is Set
Summary
Isar is an integration system for automated root filesystem generation. In versions 0.11-rc1 and 0.11, defining ISAR_APT_SNAPSHOT_DATE alone does not set the correct timestamp value for security distribution, leading to missed security updates. This issue has been patched via commit 738bcbb.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-11-19 20:27 UTC
CWE
- CWE-693 - Protection Mechanism Failure
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/ilbers/isar/security/advisorie… | x_refsource_CONFIRM |
| https://github.com/ilbers/isar/commit/3383fd808a4… | x_refsource_MISC |
| https://github.com/ilbers/isar/commit/738bcbb716c… | x_refsource_MISC |
Impacted products
{
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}
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}
],
"id": "CVE-2025-65100",
"lastModified": "2026-06-17T09:55:26.817",
"metrics": {
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"Recovery": "NOT_DEFINED",
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"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",
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"subConfidentialityImpact": "NONE",
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"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
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"version": "4.0",
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"source": "Red Hat CSAF VEX",
"status": "final",
"title": "Security Snapshot May Use Unintended Timestamp When Only ISAR_APT_SNAPSHOT_DATE Is Set",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-65100.json",
"version": "3"
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"vulnrichment": {
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}
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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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