CVE-2020-15193

Public Exploit
Memory corruption in Tensorflow

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.

Category

7.1
CVSS
Severity: High
CVSS 3.1 •
CVSS 2.0 •
EPSS 0.21%
Vendor Advisory opensuse.org
Affected: tensorflow tensorflow
Published at:
Updated at:

References

Frequently Asked Questions

What is the severity of CVE-2020-15193?
CVE-2020-15193 has been scored as a high severity vulnerability.
How to fix CVE-2020-15193?
To fix CVE-2020-15193, make sure you are using an up-to-date version of the affected component(s) by checking the vendor release notes. As for now, there are no other specific guidelines available.
Is CVE-2020-15193 being actively exploited in the wild?
It is possible that CVE-2020-15193 is being exploited or will be exploited in a near future based on public information. According to its EPSS score, there is a ~0% probability that this vulnerability will be exploited by malicious actors in the next 30 days.
What software or system is affected by CVE-2020-15193?
CVE-2020-15193 affects tensorflow tensorflow.
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