Executive Summary
Informations | |||
---|---|---|---|
Name | CVE-2020-13092 | First vendor Publication | 2020-05-15 |
Vendor | Cve | Last vendor Modification | 2024-11-21 |
Security-Database Scoring CVSS v3
Cvss vector : CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H | |||
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Overall CVSS Score | 9.8 | ||
Base Score | 9.8 | Environmental Score | 9.8 |
impact SubScore | 5.9 | Temporal Score | 9.8 |
Exploitabality Sub Score | 3.9 | ||
Attack Vector | Network | Attack Complexity | Low |
Privileges Required | None | User Interaction | None |
Scope | Unchanged | Confidentiality Impact | High |
Integrity Impact | High | Availability Impact | High |
Calculate full CVSS 3.0 Vectors scores |
Security-Database Scoring CVSS v2
Cvss vector : (AV:N/AC:L/Au:N/C:P/I:P/A:P) | |||
---|---|---|---|
Cvss Base Score | 7.5 | Attack Range | Network |
Cvss Impact Score | 6.4 | Attack Complexity | Low |
Cvss Expoit Score | 10 | Authentication | None Required |
Calculate full CVSS 2.0 Vectors scores |
Detail
scikit-learn (aka sklearn) through 0.23.0 can unserialize and execute commands from an untrusted file that is passed to the joblib.load() function, if __reduce__ makes an os.system call. NOTE: third parties dispute this issue because the joblib.load() function is documented as unsafe and it is the user's responsibility to use the function in a secure manner |
Original Source
Url : http://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13092 |
CWE : Common Weakness Enumeration
% | Id | Name |
---|---|---|
100 % | CWE-502 | Deserialization of Untrusted Data |
CPE : Common Platform Enumeration
Type | Description | Count |
---|---|---|
Application | 2 |
Sources (Detail)
Source | Url |
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Alert History
Date | Informations |
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2024-11-28 13:39:06 |
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2024-10-25 02:13:19 |
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2024-08-04 17:27:51 |
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2024-05-17 09:28:24 |
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2024-05-14 21:28:11 |
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2024-04-11 09:28:26 |
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2024-03-21 09:28:29 |
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2023-11-07 21:37:22 |
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2023-01-18 01:51:27 |
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2020-05-23 02:35:54 |
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