GSD-2019-16056
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
An issue was discovered in Python through 2.7.16, 3.x through 3.5.7, 3.6.x through 3.6.9, and 3.7.x through 3.7.4. The email module wrongly parses email addresses that contain multiple @ characters. An application that uses the email module and implements some kind of checks on the From/To headers of a message could be tricked into accepting an email address that should be denied. An attack may be the same as in CVE-2019-11340; however, this CVE applies to Python more generally.
Aliases
Aliases
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},
{
"name": "FEDORA-2019-986622833f",
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],
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},
{
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},
{
"name": "openSUSE-SU-2019:2438",
"refsource": "SUSE",
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],
"url": "http://lists.opensuse.org/opensuse-security-announce/2019-11/msg00012.html"
},
{
"name": "RHSA-2019:3725",
"refsource": "REDHAT",
"tags": [
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],
"url": "https://access.redhat.com/errata/RHSA-2019:3725"
},
{
"name": "openSUSE-SU-2019:2453",
"refsource": "SUSE",
"tags": [
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],
"url": "http://lists.opensuse.org/opensuse-security-announce/2019-11/msg00021.html"
},
{
"name": "FEDORA-2019-0d3fcae639",
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{
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},
"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 5.0,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:N/A:N",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": false
},
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6
}
},
"lastModifiedDate": "2023-02-28T14:30Z",
"publishedDate": "2019-09-06T18:15Z"
}
}
}
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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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