FKIE_CVE-2025-0508
Vulnerability from fkie_nvd - Published: 2025-03-20 10:15 - Updated: 2026-06-17 08:26
Severity
Summary
A vulnerability in the SageMaker Workflow component of aws/sagemaker-python-sdk allows for the possibility of MD5 hash collisions in all versions. This can lead to workflows being inadvertently replaced due to the reuse of results from different configurations that produce the same MD5 hash. This issue can cause integrity problems within the pipeline, potentially leading to erroneous processing outcomes.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "aws/sagemaker-python-sdk",
"vendor": "aws",
"versions": [
{
"lessThan": "4965",
"status": "affected",
"version": "unspecified",
"versionType": "custom"
}
]
}
],
"source": "security@huntr.dev"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "A vulnerability in the SageMaker Workflow component of aws/sagemaker-python-sdk allows for the possibility of MD5 hash collisions in all versions. This can lead to workflows being inadvertently replaced due to the reuse of results from different configurations that produce the same MD5 hash. This issue can cause integrity problems within the pipeline, potentially leading to erroneous processing outcomes."
},
{
"lang": "es",
"value": "Una vulnerabilidad en el componente SageMaker Workflow de aws/sagemaker-python-sdk permite la posibilidad de colisiones de hash MD5 en todas las versiones. Esto puede provocar que los flujos de trabajo se reemplacen inadvertidamente debido a la reutilizaci\u00f3n de resultados de diferentes configuraciones que generan el mismo hash MD5. Este problema puede causar problemas de integridad en la canalizaci\u00f3n, lo que podr\u00eda generar resultados de procesamiento err\u00f3neos."
}
],
"id": "CVE-2025-0508",
"lastModified": "2026-06-17T08:26:37.217",
"metrics": {
"cvssMetricV30": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.9,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.0/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:H/A:N",
"version": "3.0"
},
"exploitabilityScore": 2.2,
"impactScore": 3.6,
"source": "security@huntr.dev",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2025-0508",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-03-20T14:26:53.232640Z",
"version": "2.0.3"
}
}
]
},
"published": "2025-03-20T10:15:53.283",
"references": [
{
"source": "security@huntr.dev",
"url": "https://github.com/aws/sagemaker-python-sdk/commit/dcdd99f911e8b1a05d19cf1ad939b0fefae47864"
},
{
"source": "security@huntr.dev",
"url": "https://huntr.com/bounties/eb056818-5b81-466f-81ee-916058d34af2"
}
],
"sourceIdentifier": "security@huntr.dev",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-328"
}
],
"source": "security@huntr.dev",
"type": "Secondary"
}
]
}
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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.
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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.