CVE-2020-15193
Description
In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of dlpack.to_dlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a reinterpret_cast Since the PyObject is a Python object, not a TensorFlow Tensor, the cast to EagerTensor fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.
Risk Information
Base Score
7.1
MODERATE
Vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:L
EPSS Score
Exploitation Probability
0.215
Associated Vulnerability
| Vulnerability | OS Platform |
|---|---|
| Multiple vulnerabilities are fixed in Python-tensorflow 2.2.1 | Windows |
| Multiple vulnerabilities are fixed in Python-tensorflow 2.3.1 | Windows |
| Multiple vulnerabilities are fixed in Python-tensorflow-cpu 2.2.1 | Windows |
| Multiple vulnerabilities are fixed in Python-tensorflow-cpu 2.3.1 | Windows |
| Multiple vulnerabilities are fixed in Python-tensorflow-gpu 2.2.1 | Windows |
| Multiple vulnerabilities are fixed in Python-tensorflow-gpu 2.3.1 | Windows |
| Multiple vulnerabilities are fixed in Python-tensorflow for linux 2.2.1 | Linux |
| Multiple vulnerabilities are fixed in Python-tensorflow for linux 2.3.1 | Linux |
| Multiple vulnerabilities are fixed in Python-tensorflow-cpu for linux 2.2.1 | Linux |
| Multiple vulnerabilities are fixed in Python-tensorflow-cpu for linux 2.3.1 | Linux |
| Multiple vulnerabilities are fixed in Python-tensorflow-gpu for linux 2.2.1 | Linux |
| Multiple vulnerabilities are fixed in Python-tensorflow-gpu for linux 2.3.1 | Linux |
Patch Details
No records foundReferences
https://nvd.nist.gov/vuln/detail/CVE-2023-1234
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-1234