GSD-2022-2996
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
A flaw was found in the python-scciclient when making an HTTPS connection to a server where the server's certificate would not be verified. This issue opens up the connection to possible Man-in-the-middle (MITM) attacks.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-2996",
"description": "A flaw was found in the python-scciclient when making an HTTPS connection to a server where the server\u0027s certificate would not be verified. This issue opens up the connection to possible Man-in-the-middle (MITM) attacks.",
"id": "GSD-2022-2996",
"references": [
"https://access.redhat.com/errata/RHSA-2022:8854",
"https://access.redhat.com/errata/RHSA-2022:8868",
"https://access.redhat.com/errata/RHSA-2023:0276"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"id": "GSD-2022-2996",
"modified": "2023-12-13T01:19:19.699861Z",
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"affected_range": "\u003c0.12.0",
"affected_versions": "All versions before 0.12.0",
"cvss_v3": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:N",
"cwe_ids": [
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"CWE-937"
],
"date": "2022-09-16",
"description": "A flaw was found in the python-scciclient when making an HTTPS connection to a server where the server\u0027s certificate would not be verified. This issue opens up the connection to possible Man-in-the-middle (MITM) attacks.",
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"identifiers": [
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"CVE-2022-2996"
],
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"package_slug": "pypi/python-scciclient",
"pubdate": "2022-09-02",
"solution": "Upgrade to version 0.12.0 or above.",
"title": "Improper Certificate Validation",
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"https://opendev.org/x/python-scciclient/commit/274dca0344b65b4ac113d3271d21c17e970a636c",
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"impact": {
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.4,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.2,
"impactScore": 5.2
}
},
"lastModifiedDate": "2022-12-12T21:09Z",
"publishedDate": "2022-09-01T18:15Z"
}
}
}
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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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