Uninitialized Memory Exposure Affecting tensorflow-gpu package, versions [2.2.0, 2.2.1)[2.3.0, 2.3.1)


Severity

Recommended
0.0
high
0
10

CVSS assessment made by Snyk's Security Team. Learn more

Threat Intelligence

EPSS
0.15% (53rd percentile)

Do your applications use this vulnerable package?

In a few clicks we can analyze your entire application and see what components are vulnerable in your application, and suggest you quick fixes.

Test your applications
  • Snyk IDSNYK-PYTHON-TENSORFLOWGPU-1013560
  • published29 Sept 2020
  • disclosed28 Sept 2020
  • creditAivul Team from Qihoo 360

Introduced: 28 Sep 2020

CVE-2020-15193  (opens in a new tab)
CWE-201  (opens in a new tab)

How to fix?

Upgrade tensorflow-gpu to version 2.2.1, 2.3.1 or higher.

Overview

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Uninitialized Memory Exposure. 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_castSince the PyObject is a Python object, not a TensorFlow Tensor, the cast to EagerTensor fails.

CVSS Scores

version 3.1