CVE-2022-23573 Uninitialized variable access in Tensorflow
Attackers can trigger undefined behavior and potential code execution via uninitialized memory access in TensorFlow's AssignOp implementation.
- Exploited in the wild
- Not known
- Login needed
- Any signed-in user
- Attackable
- Over the internet
- Victim action needed
- Not needed
- Fix
- Available
What is CVE-2022-23573?
CVE-2022-23573 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 4 Feb 2022. Any signed-in user can exploit it over the network, with no action from the victim.
Official description
Tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Weakness: CWE-908
Which versions are affected by CVE-2022-23573?
22 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 1 product.
| Package | Affected | Fixed in |
|---|---|---|
tensorflow Bitnami | All versions | 2.5.3, 2.6.3, 2.7.1 |
tensorflow PyPI | All versions | 2.5.3, 2.6.3, 2.7.1 |
tensorflow PyPI | All versions | 2.5.3 |
tensorflow PyPI | All versions | 2.5.3 |
tensorflow PyPI | From 2.6.0 | 2.6.3 |
tensorflow PyPI | From 2.6.0 | 2.6.3 |
16 more packages
tensorflow PyPI | From 2.7.0 | 2.7.1 |
tensorflow PyPI | From 2.7.0 | 2.7.1 |
tensorflow-cpu PyPI | All versions | 2.5.3, 2.6.3 |
tensorflow-cpu PyPI | All versions | 2.5.3 |
tensorflow-cpu PyPI | All versions | 2.5.3 |
tensorflow-cpu PyPI | From 2.6.0 | 2.6.3 |
tensorflow-cpu PyPI | From 2.6.0 | 2.6.3 |
tensorflow-cpu PyPI | From 2.7.0 | 2.7.1 |
tensorflow-cpu PyPI | From 2.7.0 | 2.7.1 |
tensorflow-gpu PyPI | All versions | 2.5.3, 2.6.3 |
tensorflow-gpu PyPI | All versions | 2.5.3 |
tensorflow-gpu PyPI | All versions | 2.5.3 |
tensorflow-gpu PyPI | From 2.6.0 | 2.6.3 |
tensorflow-gpu PyPI | From 2.6.0 | 2.6.3 |
tensorflow-gpu PyPI | From 2.7.0 | 2.7.1 |
tensorflow-gpu PyPI | From 2.7.0 | 2.7.1 |
Products include Google Tensorflow.
How do I fix CVE-2022-23573?
Upgrade tensorflow to a fixed version for your release line: 2.5.3, 2.6.3 or 2.7.1.
Upgrade to the fixed version. Memory-safe languages, compiler hardening and fuzzing prevent most new cases. More about Memory corruption.
Is CVE-2022-23573 being exploited?
It is not confirmed as exploited in the wild, but public exploit code for CVE-2022-23573 exists.
Other vulnerabilities in Google Tensorflow
- CVE-2023-25668 TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation
- CVE-2018-7575 Integer Overflow or Wraparound in Google TensorFlow
- CVE-2021-37678 Arbitrary code execution due to YAML deserialization
- CVE-2021-35958 Path traversal in n/a
- CVE-2020-15202 Integer truncation in Shard API usage
Sources
Also known as BIT-tensorflow-2022-23573, GHSA-q85f-69q7-55h2, PYSEC-2022-137, PYSEC-2022-82, PYSEC-2026-3229.