AI Sec Watch

TensorFlow (an open source platform for machine learning) crashes when converting transposed convolutions (a type of neural network layer operation) with per-channel weight quantization (a compression technique that reduces precision individually for different channels). The crash causes a segfault (a memory access error that terminates the program), crashing the Python process.

TensorFlow, an open source platform for machine learning, has a vulnerability where a function called `Requantize` crashes when given certain types of input data (tensors of nonzero rank), allowing attackers to trigger a denial of service attack (making the system unavailable). The issue has been fixed and will be released in updated versions of the software.

TensorFlow, an open source platform for machine learning, has a bug where a specific function (`tensorflow::full_type::SubstituteFromAttrs`) crashes the program instead of properly reporting an error when it receives incorrect input (a `FullTypeDef` that doesn't have exactly three arguments). This crash could potentially be exploited to make TensorFlow applications stop working.

TensorFlow (an open source platform for machine learning) has a bug where the `RangeSize` function crashes when it receives numbers too large to fit into an `int64_t` (a 64-bit integer data type). This is caused by an integer overflow (when a number becomes too large for its data type to handle).

TensorFlow (an open source machine learning platform) crashes when a specific internal function receives null type list attributes (empty or missing type information). The developers have fixed the bug and will release the patch in upcoming versions of the software.

TensorFlow (an open source platform for machine learning) crashes when a component called mlir::tfg::GraphDefImporter::ConvertNodeDef tries to convert NodeDefs (data structures that define operations) without an operation name. This is a crash vulnerability that could cause the software to stop working unexpectedly.

TensorFlow (an open source platform for machine learning) crashes when a specific internal function called `mlir::tfg::ConvertGenericFunctionToFunctionDef` receives empty function attributes (data describing how a function should behave). This is a reachable assertion vulnerability, meaning the program encounters an unexpected condition it cannot handle.

TensorFlow, an open source machine learning platform, has a bug where a specific function crashes with a null dereference (trying to use a memory address that doesn't exist) when given empty function attributes. The issue affects multiple versions of TensorFlow and has no known workarounds.

TensorFlow, an open source platform for machine learning, has a vulnerability in its `tf.quantization.fake_quant_with_min_max_vars_gradient` function where nonscalar (multi-dimensional) input values for `min` or `max` parameters cause a CHECK fail, which is a crash that could enable a denial of service attack (disrupting service availability). The vulnerability affects multiple supported versions of TensorFlow.