AI Sec Watch

TensorFlow (an open source machine learning framework) has a vulnerability where attackers can crash TensorFlow processes by sending specially crafted data with invalid tensor types or shapes during decoding from protobuf (a data format used to serialize structured data). This is a denial of service attack, meaning the attacker can make the system stop working rather than gain unauthorized access.

TensorFlow, an open-source machine learning framework, has a bug where it can crash or behave unpredictably when decoding certain data structures (protobuf, a format for storing structured data) if some required information is missing. The problem occurs because the code only checks for this issue in debug builds (test versions), not in production builds (versions used in real applications), so real users may experience crashes or undefined behavior.

TensorFlow, an open-source machine learning framework, has a vulnerability in its Grappler component where the `set_output` function can write data to an array at any index specified by an attacker, creating a heap OOB write (out-of-bounds write, where data is written to memory locations it shouldn't access). This gives a malicious user the ability to write arbitrary data to unintended memory locations.

TensorFlow (an open-source machine learning framework) has a vulnerability where an attacker can crash the system by modifying a SavedModel file on disk to contain duplicate operation attributes, triggering an assertion failure (a built-in check that causes the program to stop if a condition is false). This is a denial of service attack (making a system unavailable to legitimate users).

TensorFlow (an open source machine learning framework) has a vulnerability where attackers can crash TensorFlow processes by providing specially crafted input when the system converts protobuf (a data format) into resource handle tensors, because a validation check can be bypassed through user-controlled arguments.

TensorFlow, an open-source machine learning framework, uses an unsafe function called `tempfile.mktemp` to create temporary files in multiple places. This creates a race condition vulnerability (TOC/TOU, a timing gap where another process can interfere between when the system checks if a filename exists and when it actually creates the file), which is especially dangerous in utility and library code rather than just testing code.

TensorFlow (an open-source framework for building machine learning models) has a vulnerability in its Range function where integer overflows (when numbers get too large and wrap around to incorrect values) can cause undefined behavior or extremely large memory allocations. This bug affects multiple versions of the software.

An attacker can create a malicious TFLite model (a compressed machine learning format for mobile devices) that writes data outside the boundaries of an array in TensorFlow, potentially overwriting the memory allocator's linked list (a data structure that tracks available memory) to achieve arbitrary write access to system memory. This vulnerability affects multiple versions of TensorFlow, an open-source framework for building AI systems.

TensorFlow, an open-source machine learning framework, has a vulnerability in TFLite (TensorFlow Lite, a lightweight version for mobile devices) where an attacker can create a specially crafted model that allows limited reads and writes outside of arrays by exploiting missing validation during conversion from sparse tensors (data structures with mostly empty values) to dense tensors (fully populated data structures). This vulnerability affects multiple versions of TensorFlow.