22307 matches found
GHSA-28JP-44VH-Q42H vulnerabilities
Vulnerabilities for packages: kubeflow-pipelines-visualization-server, tensorflow-cpu-jupyter, tensorflow-gpu-jupyter...
GHSA-MQ84-HJQX-CWF2 vulnerabilities
Vulnerabilities for packages: kubeflow-pipelines-visualization-server, tensorflow-cpu-jupyter, tensorflow-gpu-jupyter...
CVE-2025-12060 vulnerabilities
Vulnerabilities for packages: kubeflow-pipelines-visualization-server, tensorflow-cpu-jupyter, tensorflow-gpu-jupyter...
genet (>=0.11.0, <=0.17.2), mup-tf (0.0.3) +4 more potentially affected by CVE-2025-12058 via keras (2.11.0rc3)
keras PYPI version 2.11.0rc3 is affected by a known vulnerability. The following packages have a transitive dependency on keras and may be impacted: - genet =0.11.0, =2.10.0rc1, =2.10.0rc1, =2.10.0rc1, =2.11.0rc0 - tensorflow-text 2.9.0rc1 Source cves: CVE-2025-12058 Source advisory:...
tensorflow (2.6.0rc0) potentially affected by CVE-2025-12058 via keras (2.6.0rc0)
keras PYPI version 2.6.0rc0 is affected by a known vulnerability. The following packages have a transitive dependency on keras and may be impacted: - tensorflow 2.6.0rc0 Source cves: CVE-2025-12058 Source advisory: OSV:GHSA-MQ84-HJQX-CWF2...
CVE-2025-12343
A flaw was found in FFmpeg’s TensorFlow backend within the libavfilter/dnnbackendtf.c source file. The issue occurs in the dnnexecutemodeltf function, where a task object is freed multiple times in certain error-handling paths. This redundant memory deallocation can lead to a double-free conditio...
Double Free
Overview Affected versions of this package are vulnerable to Double Free in the dnnexecutemodeltf function due to multiple deallocations of a task object in certain error-handling paths. An attacker can cause the application to crash by triggering specific error conditions while processing...
tf.keras.layers.Conv2D() DoS
The vulnerability of the tf.keras.layers.Conv2D function in TensorFlow’s machine learning system is related to uncontrolled resource consumption when the padding='valid' parameter is used. Exploiting this vulnerability could allow a malicious actor to cause a service failure...
tf.keras.layers.Embedding TensorFlow
The vulnerability of the tf.keras.layers.Embedding layer in the TensorFlow machine learning system is related to the presence of undeclared capabilities during the processing of the inputdim argument. Exploiting this vulnerability could allow attackers to compromise the integrity and accessibilit...
EUVD-2021-0383
Malware in sbrugna...
EUVD-2021-0394
Malware in sbrugna...
EUVD-2021-0288
Malware in sbrugna...
EUVD-2021-0446
Malware in sbrugna...
EUVD-2021-0400
Malware in sbrugna...
EUVD-2021-0316
Malware in sbrugna...
EUVD-2021-0452
Malware in sbrugna...
EUVD-2019-0139
Malware in sbrugna...
EUVD-2019-0142
Malware in sbrugna...