FKIE_CVE-2026-22870
Vulnerability from fkie_nvd - Published: 2026-01-13 21:15 - Updated: 2026-06-17 10:20
Severity
Summary
GuardDog is a CLI tool to identify malicious PyPI packages. Prior to 2.7.1, GuardDog's safe_extract() function does not validate decompressed file sizes when extracting ZIP archives (wheels, eggs), allowing attackers to cause denial of service through zip bombs. A malicious package can consume gigabytes of disk space from a few megabytes of compressed data. This vulnerability is fixed in 2.7.1.
References
{
"affected": [
{
"affectedData": [
{
"product": "guarddog",
"vendor": "DataDog",
"versions": [
{
"status": "affected",
"version": "\u003c 2.7.1"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:datadoghq:guarddog:*:*:*:*:*:python:*:*",
"matchCriteriaId": "872A0822-90B4-4E21-985F-788BE44CA582",
"versionEndExcluding": "2.7.1",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "GuardDog is a CLI tool to identify malicious PyPI packages. Prior to 2.7.1, GuardDog\u0027s safe_extract() function does not validate decompressed file sizes when extracting ZIP archives (wheels, eggs), allowing attackers to cause denial of service through zip bombs. A malicious package can consume gigabytes of disk space from a few megabytes of compressed data. This vulnerability is fixed in 2.7.1."
},
{
"lang": "es",
"value": "GuardDog es una herramienta CLI para identificar paquetes PyPI maliciosos. Antes de la versi\u00f3n 2.7.1, la funci\u00f3n safe_extract() de GuardDog no valida los tama\u00f1os de archivo descomprimidos al extraer archivos ZIP (wheels, eggs), permitiendo a los atacantes causar denegaci\u00f3n de servicio mediante bombas ZIP. Un paquete malicioso puede consumir gigabytes de espacio en disco a partir de unos pocos megabytes de datos comprimidos. Esta vulnerabilidad est\u00e1 corregida en la versi\u00f3n 2.7.1."
}
],
"id": "CVE-2026-22870",
"lastModified": "2026-06-17T10:20:32.390",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"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"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "nvd@nist.gov",
"type": "Primary"
}
],
"cvssMetricV40": [
{
"cvssData": {
"Automatable": "NOT_DEFINED",
"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "LOW",
"attackRequirements": "NONE",
"attackVector": "NETWORK",
"availabilityRequirement": "NOT_DEFINED",
"baseScore": 7.1,
"baseSeverity": "HIGH",
"confidentialityRequirement": "NOT_DEFINED",
"exploitMaturity": "NOT_DEFINED",
"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": "PASSIVE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N/E:X/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": "HIGH",
"vulnConfidentialityImpact": "NONE",
"vulnIntegrityImpact": "NONE",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-22870",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-01-13T21:23:44.801869Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-01-13T21:15:55.067",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/DataDog/guarddog/commit/c3fb07b4838945f42497e78b7a02bcfb1e63969b"
},
{
"source": "security-advisories@github.com",
"tags": [
"Exploit",
"Third Party Advisory"
],
"url": "https://github.com/DataDog/guarddog/security/advisories/GHSA-ffj4-jq7m-9g6v"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-409"
}
],
"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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