FKIE_CVE-2026-63311
Vulnerability from fkie_nvd - Published: 2026-08-22 15:16 - Updated: 2026-10-14 13:10
Severity
Summary
NLTK before 3.10.0 (affected versions <= 3.9.4) contains a server-side request forgery (SSRF) vulnerability in the validate_network_url() function in nltk/pathsec.py. The _resolve_hostname() helper catches OSError and ValueError during socket.getaddrinfo() and returns an empty list; when DNS resolution fails, the validation loop executes no IP checks and the function fails open, allowing urlopen() to proceed without validation. An attacker who can trigger DNS resolution failures or use DNS rebinding can bypass SSRF protections and reach restricted network resources, including cloud metadata endpoints (e.g., 169.254.169.254).
References
| URL | Tags | ||
|---|---|---|---|
| disclosure@vulncheck.com | https://github.com/nltk/nltk/security/advisories/GHSA-3gqm-fcw5-w839 | Exploit, Mitigation, Vendor Advisory | |
| disclosure@vulncheck.com | https://www.vulncheck.com/advisories/nltk-before-ssrf-via-dns-resolution-failure | Third Party Advisory |
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"packageURL": "pkg:pypi/nltk",
"product": "nltk",
"vendor": "nltk",
"versions": [
{
"lessThan": "3.10.0",
"status": "affected",
"version": "0",
"versionType": "semver"
},
{
"status": "unaffected",
"version": "3.10.0",
"versionType": "semver"
}
]
}
],
"source": "disclosure@vulncheck.com"
}
],
"configurations": [
{
"nodes": [
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"criteria": "cpe:2.3:a:nltk:nltk:*:*:*:*:*:*:*:*",
"matchCriteriaId": "653A48E1-BCC5-4342-B5DF-E8D71A83BCB9",
"versionEndExcluding": "3.10.0",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
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],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "NLTK before 3.10.0 (affected versions \u003c= 3.9.4) contains a server-side request forgery (SSRF) vulnerability in the validate_network_url() function in nltk/pathsec.py. The _resolve_hostname() helper catches OSError and ValueError during socket.getaddrinfo() and returns an empty list; when DNS resolution fails, the validation loop executes no IP checks and the function fails open, allowing urlopen() to proceed without validation. An attacker who can trigger DNS resolution failures or use DNS rebinding can bypass SSRF protections and reach restricted network resources, including cloud metadata endpoints (e.g., 169.254.169.254)."
},
{
"lang": "es",
"value": "NLTK anterior a 3.10.0 (versiones afectadas menor o igual que 3.9.4) contiene una vulnerabilidad de falsificaci\u00f3n de petici\u00f3n del lado del servidor (SSRF) en la funci\u00f3n validate_network_url() en nltk/pathsec.py. La funci\u00f3n auxiliar _resolve_hostname() captura OSError y ValueError durante socket.getaddrinfo() y devuelve una lista vac\u00eda; cuando la resoluci\u00f3n DNS falla, el bucle de validaci\u00f3n no ejecuta comprobaciones de IP y la funci\u00f3n falla abierta, permitiendo que urlopen() proceda sin validaci\u00f3n. Un atacante que puede desencadenar fallos de resoluci\u00f3n DNS o usar el reenlace DNS puede eludir las protecciones SSRF y alcanzar recursos de red restringidos, incluyendo puntos finales de metadatos en la nube (p. ej., 169.254.169.254)."
}
],
"id": "CVE-2026-63311",
"lastModified": "2026-10-14T13:10:00.187",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 1.4,
"source": "disclosure@vulncheck.com",
"type": "Secondary"
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"cvssMetricV40": [
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"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "LOW",
"attackRequirements": "NONE",
"attackVector": "NETWORK",
"availabilityRequirement": "NOT_DEFINED",
"baseScore": 6.9,
"baseSeverity": "MEDIUM",
"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": "LOW",
"subConfidentialityImpact": "LOW",
"subIntegrityImpact": "LOW",
"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:L/VA:N/SC:L/SI:L/SA:L/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": "NONE",
"vulnConfidentialityImpact": "NONE",
"vulnIntegrityImpact": "LOW",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"source": "disclosure@vulncheck.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-63311",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-08-26T15:58:19.898915Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-08-22T15:16:19.233",
"references": [
{
"source": "disclosure@vulncheck.com",
"tags": [
"Exploit",
"Mitigation",
"Vendor Advisory"
],
"url": "https://github.com/nltk/nltk/security/advisories/GHSA-3gqm-fcw5-w839"
},
{
"source": "disclosure@vulncheck.com",
"tags": [
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],
"url": "https://www.vulncheck.com/advisories/nltk-before-ssrf-via-dns-resolution-failure"
}
],
"sourceIdentifier": "disclosure@vulncheck.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-918"
}
],
"source": "disclosure@vulncheck.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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