AI & LLM Vulnerabilities

TensorFlow (an open source machine learning platform) has a bug where certain inputs with incorrect dimensions crash the SdcaOptimizer component due to a failed validation check. This happens when `dense_features` or `example_state_data` inputs don't have the expected 2D structure (rank 2, meaning a table with rows and columns).

TensorFlow, an open source machine learning platform, crashes when a function called `SparseFillEmptyRowsGrad` receives empty inputs instead of data. This happens because the code doesn't properly validate (check) what data it receives before trying to process it.

TensorFlow (an open-source machine learning platform) crashes when a function called `FractionMaxPoolGrad` receives oversized inputs for `row_pooling_sequence` and `col_pooling_sequence` parameters. This is caused by an out-of-bounds read (accessing memory locations outside the intended range), which allows the program to fail unexpectedly.

TensorFlow (an open-source platform for machine learning) has a vulnerability where a function called `ThreadUnsafeUnigramCandidateSampler` crashes if it receives an input value for `filterbank_channel_count` that exceeds the maximum allowed size. This is caused by improper input validation (failure to check that user-provided values are within acceptable limits).

TensorFlow, an open source machine learning platform, has a vulnerability where the `MirrorPadGrad` function crashes with a heap OOB error (out-of-bounds memory access, where the software tries to read memory it shouldn't) when given incorrectly sized input padding values. This bug allows attackers to potentially crash TensorFlow applications.

TensorFlow, an open source machine learning platform, has a vulnerability in the `tf.raw_ops.TensorListResize` function where providing a nonscalar value (a value that isn't a single number) for the `size` input causes a CHECK fail, which can be exploited to trigger a denial of service attack (making the system crash or become unavailable).

TensorFlow, an open source machine learning platform, has a vulnerability where a specific function called `tf.raw_ops.TensorListConcat` crashes with a segmentation fault (a memory error that causes a program to suddenly stop) when given certain invalid input. This crash can be exploited to cause a denial of service attack (making the service unavailable to users).

TensorFlow is a machine learning platform that had a bug where a function called `BCast::ToShape` would crash when given very large numbers (larger than an `int32`, which is a 32-bit integer) even though it was designed to handle even larger numbers called `int64`. This bug could be triggered by using the `tf.experimental.numpy.outer` function with large inputs.

TensorFlow, an open source machine learning platform, had a bug where passing quantized tensors (specially compressed numeric data) to certain functions caused the parsing code to fail silently and return a null pointer (empty reference) instead of the expected data. This could cause crashes or unexpected behavior in machine learning programs using affected TensorFlow functions.

TensorFlow, an open source machine learning platform, has a vulnerability in its `tf.image.generate_bounding_box_proposals` function when running on GPU. The function fails to validate that the `scores` input has the correct rank (dimension structure), which could cause problems. This is classified as improper input validation (CWE-20, where a program doesn't properly check if data meets required specifications).

TensorFlow's poisson loss function (a tool for measuring prediction errors in machine learning) crashes when certain input dimensions multiply together and exceed the limit of a 32-bit integer, causing a size mismatch during broadcast assignment (aligning data for computation). The vulnerability affects multiple versions of TensorFlow.

TensorFlow (an open source platform for machine learning) has a bug in the `tf.raw_ops.ImageProjectiveTransformV2` function where it overflows (uses more memory than available) when given a large output shape. This vulnerability was caused by an incorrect calculation of buffer size (the amount of memory needed to store data).

TensorFlow (an open source machine learning platform) has a vulnerability in the `tf.raw_ops.FusedResizeAndPadConv2D` function where a buffer overflow (a memory error where data exceeds available space) occurs when given very large tensor shapes. The bug stems from an incorrect buffer size calculation.

TensorFlow, an open source machine learning platform, has a bug where creating a numpy array (a data structure for storing numbers) with a specific shape (one dimension with zero elements and others summing to a large number) causes an error. The developers have created a fix and will release it in upcoming versions of TensorFlow.

TensorFlow, an open source machine learning platform, has a vulnerability in the `BaseCandidateSamplerOp` function that causes a heap OOB read (out-of-bounds read, where a program accesses memory it shouldn't) when it receives certain invalid input values. This is a memory safety bug that could allow attackers to read sensitive data from the program's memory.

TensorFlow (an open source platform for machine learning) has a bug where certain operations crash when they receive a different number of inputs than expected, which could cause the program to stop working. This vulnerability is classified as an out-of-bounds read (accessing memory outside the intended range).

Deeplearning4J (a tool for building machine learning models on Java systems) versions up to 1.0.0-M2.1 have a vulnerability where some test code references unclaimed S3 buckets (cloud storage spaces that no longer belong to the original owner), which could potentially be exploited by attackers who claim those buckets. This mainly affects older natural language processing examples in the software.

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.