GSD-2019-18348
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
An issue was discovered in urllib2 in Python 2.x through 2.7.17 and urllib in Python 3.x through 3.8.0. CRLF injection is possible if the attacker controls a url parameter, as demonstrated by the first argument to urllib.request.urlopen with \r\n (specifically in the host component of a URL) followed by an HTTP header. This is similar to the CVE-2019-9740 query string issue and the CVE-2019-9947 path string issue. (This is not exploitable when glibc has CVE-2016-10739 fixed.). This is fixed in: v2.7.18, v2.7.18rc1; v3.5.10, v3.5.10rc1; v3.6.11, v3.6.11rc1, v3.6.12; v3.7.8, v3.7.8rc1, v3.7.9; v3.8.3, v3.8.3rc1, v3.8.4, v3.8.4rc1, v3.8.5, v3.8.6, v3.8.6rc1.
Aliases
Aliases
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"description": "An issue was discovered in urllib2 in Python 2.x through 2.7.17 and urllib in Python 3.x through 3.8.0. CRLF injection is possible if the attacker controls a url parameter, as demonstrated by the first argument to urllib.request.urlopen with \\r\\n (specifically in the host component of a URL) followed by an HTTP header. This is similar to the CVE-2019-9740 query string issue and the CVE-2019-9947 path string issue. (This is not exploitable when glibc has CVE-2016-10739 fixed.). This is fixed in: v2.7.18, v2.7.18rc1; v3.5.10, v3.5.10rc1; v3.6.11, v3.6.11rc1, v3.6.12; v3.7.8, v3.7.8rc1, v3.7.9; v3.8.3, v3.8.3rc1, v3.8.4, v3.8.4rc1, v3.8.5, v3.8.6, v3.8.6rc1.",
"id": "GSD-2019-18348",
"references": [
"https://www.suse.com/security/cve/CVE-2019-18348.html",
"https://access.redhat.com/errata/RHSA-2020:4285",
"https://access.redhat.com/errata/RHSA-2020:4273",
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"https://alas.aws.amazon.com/cve/html/CVE-2019-18348.html"
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"impact": {
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"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:N/C:N/I:P/A:N",
"version": "2.0"
},
"exploitabilityScore": 8.6,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": true
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"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N",
"version": "3.1"
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"lastModifiedDate": "2020-11-16T20:46Z",
"publishedDate": "2019-10-23T17:15Z"
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}
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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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