CVE-2022-23595: Null pointer dereference in TensorFlow
First published: Fri Feb 04 2022(Updated: )
### Impact When [building an XLA compilation cache](https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/jit/xla_platform_info.cc#L43-L104), if default settings are used, TensorFlow triggers a null pointer dereference: ```cc string allowed_gpus = flr->config_proto()->gpu_options().visible_device_list(); ``` In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`. ### Patches We have patched the issue in GitHub commit [e21af685e1828f7ca65038307df5cc06de4479e8](https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8). 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. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | <=2.5.2 | |
| Google TensorFlow | >=2.6.0<=2.6.2 | |
| Google TensorFlow | =2.7.0 | |
| pip/tensorflow-gpu | =2.7.0 | 2.7.1 |
| pip/tensorflow-gpu | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow-gpu | <2.5.3 | 2.5.3 |
| pip/tensorflow-cpu | =2.7.0 | 2.7.1 |
| pip/tensorflow-cpu | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow-cpu | <2.5.3 | 2.5.3 |
| pip/tensorflow | =2.7.0 | 2.7.1 |
| pip/tensorflow | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow | <2.5.3 | 2.5.3 |
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Reference Links
- https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/jit/xla_platform_info.cc#L43-L104
- https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx
- https://nvd.nist.gov/vuln/detail/CVE-2022-23595
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-103.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-158.yaml
- https://github.com/advisories/GHSA-fpcp-9h7m-ffpx (Advisory)
Frequently Asked Questions
What is the severity of CVE-2022-23595?
The severity of CVE-2022-23595 is classified as medium due to the potential for a denial of service through a null pointer dereference.
How do I fix CVE-2022-23595?
To fix CVE-2022-23595, upgrade TensorFlow to version 2.5.3, 2.6.3, or 2.7.1, depending on your currently installed version.
What versions of TensorFlow are affected by CVE-2022-23595?
CVE-2022-23595 affects TensorFlow versions 2.5.2 and earlier as well as versions from 2.6.0 to 2.6.2 and 2.7.0.
What is the impact of CVE-2022-23595?
The impact of CVE-2022-23595 includes potential denial of service due to application crashes resulting from a null pointer dereference.
Is CVE-2022-23595 remote exploitability?
CVE-2022-23595 is not considered remotely exploitable as it requires specific conditions to trigger the vulnerability.
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