Memory corruption in Tensorflow
- Published
- Last modified
- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean? →
- published
- Whether CISA lists it in its Known Exploited Vulnerabilities catalog: proof that attackers are using it.What do these mean? →
- Not listed in CISA KEV
- Fix
- Fixed in 2.2.1, 2.3.1
- Whether working attack code is public (Exploit-DB, Metasploit, Nuclei or GitHub). Public code makes attacks easier.What do these mean? →
- None on Exploit-DB, Metasploit or GitHub
Summary
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.
Timeline
- CVE published
- Record last updated
Affected versions
- tensorflow tensorflow
2.2.0, 2.3.0
- tensorflowPyPI
fixed in 2.2.1; From 2.3.0rc0 · fixed in 2.3.1
- tensorflow-cpuPyPI
fixed in 2.2.1; From 2.3.0rc0 · fixed in 2.3.1
- tensorflow-gpuPyPI
fixed in 2.2.1; From 2.3.0rc0 · fixed in 2.3.1
- tensorflowPyPI
From 2.2.0 · fixed in 2.2.1
- tensorflowPyPI
From 2.3.0 · fixed in 2.3.1
- tensorflow-cpuPyPI
From 2.2.0 · fixed in 2.2.1
- tensorflow-cpuPyPI
From 2.3.0 · fixed in 2.3.1
- tensorflow-gpuPyPI
From 2.2.0 · fixed in 2.2.1
- tensorflow-gpuPyPI
From 2.3.0 · fixed in 2.3.1