CVE-2022-23563: TensorFlow: TOC/TOU race allows temp file hijacking

Tensorflow is an Open Source Machine Learning Framework. In multiple places, TensorFlow uses `tempfile.mktemp` to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in `mktemp` and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the `mktemp` function usage. We have patched the issue in several commits, replacing `mktemp` with the safer `mkstemp`/`mkdtemp` functions, according to the usage pattern. Users are advised to upgrade as soon as possible.

Attacker has low-privilege shell access to a shared GPU training server (common in university labs, HPC clusters, enterprise data science teams). They run a background watcher monitoring /tmp for filenames matching TensorFlow's temp naming patterns using inotify. A privileged ML pipeline job calls mktemp(), which returns a filename but does not yet create it. In the microsecond window before TensorFlow's next operation, the attacker creates a symlink at that exact path pointing to a sensitive target — for example, a cron file, a model registry path, or a world-readable location. TensorFlow proceeds unaware, writing training data or checkpoint content to the attacker's target. In the mkdtemp-should-have-been-used case, TensorFlow creates a file instead of a directory, causing crashes that mask the underlying manipulation. Net result: training data exfiltration or integrity compromise of model artifacts, depending on attacker objective.