Search for packages
| purl | pkg:pypi/scikit-learn@0.23.1 |
| Vulnerability | Summary | Fixed by |
|---|---|---|
|
VCID-eugr-crnj-77dc
Aliases: CVE-2024-5206 GHSA-jw8x-6495-233v PYSEC-2024-110 |
Affected by 0 other vulnerabilities. |
|
|
VCID-fd91-hz39-73fy
Aliases: CVE-2020-28975 GHSA-jxfp-4rvq-9h9m PYSEC-2020-108 |
svm_predict_values in svm.cpp in Libsvm v324, as used in scikit-learn 0.23.2 and other products, allows attackers to cause a denial of service (segmentation fault) via a crafted model SVM (introduced via pickle, json, or any other model permanence standard) with a large value in the _n_support array. NOTE: the scikit-learn vendor's position is that the behavior can only occur if the library's API is violated by an application that changes a private attribute. |
Affected by 0 other vulnerabilities. Affected by 1 other vulnerability. |
| Vulnerability | Summary | Aliases |
|---|---|---|
| VCID-fcrh-qvee-ryhf | 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 |
CVE-2020-13092
GHSA-jjw5-xxj6-pcv5 PYSEC-2020-107 |