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Advisory Package Curation

CVE-2022-23595

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Advisory Summaries

github_osv/GHSA-fpcp-9h7m-ffpx

Null pointer dereference in TensorFlow ### 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.

gitlab/pypi/tensorflow-cpu/CVE-2022-23595

Null pointer dereference in TensorFlow 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`.

gitlab/pypi/tensorflow-gpu/CVE-2022-23595

Null pointer dereference in TensorFlow 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`.

pypa/tensorflow-cpu/PYSEC-2022-103

Tensorflow is an Open Source Machine Learning Framework. When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`. 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.

pypa/tensorflow-gpu/PYSEC-2022-158

Tensorflow is an Open Source Machine Learning Framework. When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`. 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.

pysec/PYSEC-2022-103

Tensorflow is an Open Source Machine Learning Framework. When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`. 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.

pysec/PYSEC-2022-158

Tensorflow is an Open Source Machine Learning Framework. When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`. 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.