FKIE_CVE-2026-46383
Vulnerability from fkie_nvd - Published: 2026-05-15 17:16 - Updated: 2026-06-17 10:53
Severity
Summary
Microsoft APM is an open-source, community-driven dependency manager for AI agents. Prior to 0.13.0, Microsoft APM contains a Windows-specific archive extraction boundary failure in the legacy-bundle probe used by apm install <bundle> on supported Python 3.10 and 3.11 runtimes. When apm install is given a local .tar.gz that is not recognized as a plugin-format bundle, APM probes whether it is a legacy --format apm bundle. On Python versions earlier than 3.12, that probe extracts untrusted tar members with raw tar.extractall() without rejecting Windows absolute member names such as D:/.... This vulnerability is fixed in 0.13.0.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "apm",
"vendor": "microsoft",
"versions": [
{
"status": "affected",
"version": "\u003c 0.13.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Microsoft APM is an open-source, community-driven dependency manager for AI agents. Prior to 0.13.0, Microsoft APM contains a Windows-specific archive extraction boundary failure in the legacy-bundle probe used by apm install \u003cbundle\u003e on supported Python 3.10 and 3.11 runtimes. When apm install is given a local .tar.gz that is not recognized as a plugin-format bundle, APM probes whether it is a legacy --format apm bundle. On Python versions earlier than 3.12, that probe extracts untrusted tar members with raw tar.extractall() without rejecting Windows absolute member names such as D:/.... This vulnerability is fixed in 0.13.0."
}
],
"id": "CVE-2026-46383",
"lastModified": "2026-06-17T10:53:37.220",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 5.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 1.8,
"impactScore": 3.6,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-46383",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-05-15T17:48:40.768904Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-05-15T17:16:49.090",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/microsoft/apm/security/advisories/GHSA-mq5j-pw29-jcv3"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"url": "https://github.com/microsoft/apm/security/advisories/GHSA-mq5j-pw29-jcv3"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-22"
},
{
"lang": "en",
"value": "CWE-73"
}
],
"source": "security-advisories@github.com",
"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.