FKIE_CVE-2026-19954
Vulnerability from fkie_nvd - Published: 2026-10-05 07:16 - Updated: 2026-10-06 18:16
Severity
Summary
Net::Whois::Raw versions before 2.99044 for Perl ship a pwhois command-line tool that queries WHOIS for the wrong domain for unicode domain names.
pwhois encodes each non-ASCII label directly using Net::IDN::Punycode and prepends xn--. Apart from lowercasing ASCII and Cyrillic letters, it skips the IDNA mapping and normalization steps, so a label with other uppercase letters, or not in NFC, encodes to a different A-label than its IDNA form. For example, a label of U+00C9 followed by "cole" encodes to "xn--cole-pka" rather than "xn--cole-9oa".
The Net::Whois::Raw library modules are not affected.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"collectionURL": "https://cpan.org/modules",
"defaultStatus": "unaffected",
"modules": [
"Net::Whois::Raw"
],
"packageName": "Net-Whois-Raw",
"packageURL": "pkg:cpan/Net-Whois-Raw",
"programFiles": [
"bin/pwhois"
],
"programRoutines": [
{
"name": "to_punycode"
}
],
"repo": "https://github.com/regru/Net-Whois-Raw",
"versions": [
{
"lessThan": "2.99044",
"status": "affected",
"version": "0",
"versionType": "custom"
}
]
}
],
"source": "9b29abf9-4ab0-4765-b253-1875cd9b441e"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Net::Whois::Raw versions before 2.99044 for Perl ship a pwhois command-line tool that queries WHOIS for the wrong domain for unicode domain names.\n\npwhois encodes each non-ASCII label directly using Net::IDN::Punycode and prepends xn--. Apart from lowercasing ASCII and Cyrillic letters, it skips the IDNA mapping and normalization steps, so a label with other uppercase letters, or not in NFC, encodes to a different A-label than its IDNA form. For example, a label of U+00C9 followed by \"cole\" encodes to \"xn--cole-pka\" rather than \"xn--cole-9oa\".\n\nThe Net::Whois::Raw library modules are not affected."
}
],
"id": "CVE-2026-19954",
"lastModified": "2026-10-06T18:16:53.823",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.4,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 2.5,
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-19954",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-10-06T17:52:04.047979Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-10-05T07:16:30.820",
"references": [
{
"source": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
"url": "https://github.com/regru/Net-Whois-Raw/issues/34"
},
{
"source": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
"url": "https://github.com/regru/Net-Whois-Raw/pull/35"
},
{
"source": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
"url": "https://metacpan.org/release/NALOBIN/Net-Whois-Raw-2.99044/changes"
},
{
"source": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
"url": "https://metacpan.org/release/PJCJ/Net-IDN-Encode-2.590-TRIAL/view/lib/Net/IDN/Punycode.pm#WARNING"
},
{
"source": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
"url": "https://security.metacpan.org/patches/N/Net-Whois-Raw/2.99043/CVE-2026-19954-r1.patch"
},
{
"source": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
"url": "https://www.rfc-editor.org/rfc/rfc5891#section-5.2"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://www.openwall.com/lists/oss-security/2026/10/05/9"
}
],
"sourceIdentifier": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-176"
}
],
"source": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
"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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