The Keras Model.load_model method can be exploited to achieve arbitrary code execution, even with safe_mode=True. One can create a specially crafted .h5/.hdf5 model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafting a special .h5 archive file that uses the Lambda layer feature of keras which allows arbitrary Python code in the form of pickled code. The vulnerability comes from the fact that the safe_mode=True option is not honored when reading .h5 archives. Note that the .h5/.hdf5 format is a legacy format supported by Keras 3 for backwards compatibility.

A flaw was found in Libtiff. This vulnerability is a "write-what-where" condition, triggered when the library processes a specially crafted TIFF image file. By providing an abnormally large image height value in the file's metadata, an attacker can trick the library into writing attacker-controlled color data to an arbitrary memory location. This memory corruption can be exploited to cause a denial of service (application crash) or to achieve arbitrary code execution with the permissions of the user.

A flaw was found in the Udisks daemon, where it allows unprivileged users to create loop devices using the D-BUS system. This is achieved via the loop device handler, which handles requests sent through the D-BUS interface. As two of the parameters of this handle, it receives the file descriptor list and index specifying the file where the loop device should be backed. The function itself validates the index value to ensure it isn't bigger than the maximum value allowed. However, it fails to validate the lower bound, allowing the index parameter to be a negative value. Under these circumstances, an attacker can cause the UDisks daemon to crash or perform a local privilege escalation by gaining access to files owned by privileged users.