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tensorflow-gpu vulnerabilities · Page 6

Vulnerabilities published for tensorflow-gpu in the last 30 days: 0. Listed in the KEV catalogue: 0.

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Vulnerabilities: 427

  1. CVE-2022-35934

    Medium · CVSS 5.9EPSS 0.48%Fix available

    `CHECK` failure in tf.reshape in Tensorflow

    TensorFlow is an open source platform for machine learning. The implementation of tf.reshape op in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This issue has been patched in GitHub commit 61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.7.2
    • Update to 2.8.1
    • Update to 2.9.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  2. GHSA-mw6j-hh29-h379

    Not ratedFix available

    `CHECK` failure in depthwise ops via overflows

    ### Impact The implementation of depthwise ops in TensorFlow is vulnerable to a denial of service via `CHECK`-failure (assertion failure) caused by overflowing the number of elements in a tensor: ```python import tensorflow as tf input = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32) filter_sizes = tf.constant(1879048192, shape=[13], dtype=tf.int32) out_backprop = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32) tf.raw_ops.DepthwiseConv2dNativeBackpropFilter( input=input, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 1, 1, 1], padding="SAME") ``` This is another instance of [TFSA-2021-198](https://github

    tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  3. CVE-2022-29216

    High · CVSS 7.8EPSS 0.55%Fix available

    Code injection in `saved_model_cli` in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, TensorFlow's `saved_model_cli` tool is vulnerable to a code injection. This can be used to open a reverse shell. This code path was maintained for compatibility reasons as the maintainers had several test cases where numpy expressions were used as arguments. However, given that the tool is always run manually, the impact of this is still not severe. The maintainers have now removed the `safe=False` argument, so all parsing is done without calling `eval`. The patch is available in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  4. CVE-2022-29213

    Medium · CVSS 5.5EPSS 0.32%Fix available

    Incomplete validation in signal ops leads to crashes in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the `tf.compat.v1.signal.rfft2d` and `tf.compat.v1.signal.rfft3d` lack input validation and under certain condition can result in crashes (due to `CHECK`-failures). Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  5. CVE-2022-29210

    Medium · CVSS 5.5EPSS 0.23%Fix available

    Heap buffer overflow due to incorrect hash function in TensorFlow

    TensorFlow is an open source platform for machine learning. In version 2.8.0, the `TensorKey` hash function used total estimated `AllocatedBytes()`, which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. `int32_t`). It also tried to access individual tensor bytes through `tensor.data()` of size `AllocatedBytes()`. This led to ASAN failures because the `AllocatedBytes()` is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the `.data()` buffer. The discoverers could not use this byte vector anyway because types s

    tensorflow-gpuPython
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  6. CVE-2022-29209

    Medium · CVSS 5.5EPSS 0.4%Fix available

    Type confusion leading to `CHECK`-failure based denial of service in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the macros that TensorFlow uses for writing assertions (e.g., `CHECK_LT`, `CHECK_GT`, etc.) have an incorrect logic when comparing `size_t` and `int` values. Due to type conversion rules, several of the macros would trigger incorrectly. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  7. CVE-2022-29211

    Medium · CVSS 5.5EPSS 0.32%Fix available

    Segfault in TensorFlow if `tf.histogram_fixed_width` is called with NaN values

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  8. CVE-2022-29212

    Medium · CVSS 5.5EPSS 0.32%Fix available

    Core dump when loading TFLite models with quantization in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling. Thus, since code was calling `QuantizeMultiplierSmallerThanOneExp`, the `TFLITE_CHECK_LT` assertion would trigger and abort the process. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  9. CVE-2022-29201

    Medium · CVSS 5.5EPSS 0.34%Fix available

    Missing validation in `QuantizedConv2D` results in undefined behavior in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.QuantizedConv2D` does not fully validate the input arguments. In this case, references get bound to `nullptr` for each argument that is empty. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  10. CVE-2022-29202

    Medium · CVSS 5.5EPSS 0.32%Fix available

    Denial of service in TensorFlow due to lack of validation in `tf.ragged.constant`

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.ragged.constant` does not fully validate the input arguments. This results in a denial of service by consuming all available memory. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  11. CVE-2022-29203

    Medium · CVSS 5.5EPSS 0.34%Fix available

    Integer overflow in `SpaceToBatchND` in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SpaceToBatchND` (in all backends such as XLA and handwritten kernels) is vulnerable to an integer overflow: The result of this integer overflow is used to allocate the output tensor, hence we get a denial of service via a `CHECK`-failure (assertion failure), as in TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  12. CVE-2022-29204

    Medium · CVSS 5.5EPSS 0.35%Fix available

    Missing validation causes denial of service in TensorFlow via `Conv3DBackpropFilterV2`

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a `CHECK`-failure (assertion failure), as per TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  13. CVE-2022-29208

    High · CVSS 7.1EPSS 0.39%Fix available

    Segfault and Out-of-bounds Write write due to incomplete validation in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.EditDistance` has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service. In multiple places throughout the code, one may compute an index for a write operation. However, the existing validation only checks against the upper bound of the array. Hence, it is possible to write before the array by massaging the input to generate negative values for `loc`. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  14. CVE-2022-29205

    Medium · CVSS 5.5EPSS 0.33%Fix available

    Segfault due to missing support for quantized types in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, there is a potential for segfault / denial of service in TensorFlow by calling `tf.compat.v1.*` ops which don't yet have support for quantized types, which was added after migration to TensorFlow 2.x. In these scenarios, since the kernel is missing, a `nullptr` value is passed to `ParseDimensionValue` for the `py_value` argument. Then, this is dereferenced, resulting in segfault. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  15. CVE-2022-29206

    Medium · CVSS 5.5EPSS 0.36%Fix available

    Missing validation results in undefined behavior in `SparseTensorDenseAdd` in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorDenseAdd` does not fully validate the input arguments. In this case, a reference gets bound to a `nullptr` during kernel execution. This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  16. CVE-2022-29207

    Medium · CVSS 5.5EPSS 0.33%Fix available

    Undefined behavior when users supply invalid resource handles in TensorFlow

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid. In graph mode, it would have been impossible to perform these API calls, but migration to TF 2.x eager mode opened up this vulnerability. If the resource handle is empty, then a reference is bound to a null pointer inside TensorFlow codebase (various codepaths). This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  17. CVE-2022-29195

    Medium · CVSS 5.5EPSS 0.33%Fix available

    Missing validation causes denial of service in TensorFlow via `StagePeek`

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.StagePeek` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `index` is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  18. CVE-2022-29197

    Medium · CVSS 5.5EPSS 0.33%Fix available

    Missing validation causes denial of service in TensorFlow via `UnsortedSegmentJoin`

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  19. CVE-2022-29196

    Medium · CVSS 5.5EPSS 0.33%Fix available

    Missing validation causes denial of service in TensorFlow via `Conv3DBackpropFilterV2`

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.Conv3DBackpropFilterV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code does not validate that the `filter_sizes` argument is a vector. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

  20. CVE-2022-29198

    Medium · CVSS 5.5EPSS 0.33%Fix available

    Missing validation causes denial of service in TensorFlow via `SparseTensorToCSRSparseMatrix`

    TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

    tensorflow-gpu tensorflow-gpu tensorflow-gpu tensorflow-gpuPython
    • Update to 2.6.4
    • Update to 2.7.2
    • Update to 2.8.1
    Published Updated
    Source advisory 1 ↗Source advisory 2 ↗Source advisory 3 ↗

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