PyPI package
tensorflow-gpu
pkg:pypi/tensorflow-gpu
Vulnerabilities (421)
| CVE | Sev | CVSS | KEV | Affected versions | Fixed in | Published | Description |
|---|---|---|---|---|---|---|---|
| CVE-2020-15211 | — | < 1.15.4 | 1.15.4 | Sep 25, 2020 | In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer f | ||
| CVE-2020-15191 | — | >= 2.2.0, < 2.2.1 | 2.2.1 | Sep 25, 2020 | In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argument is not properly c | ||
| CVE-2020-15192 | — | >= 2.2.0, < 2.2.1 | 2.2.1 | Sep 25, 2020 | In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly checked. Since each o | ||
| CVE-2020-15193 | — | >= 2.2.0, < 2.2.1 | 2.2.1 | Sep 25, 2020 | In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping | ||
| CVE-2020-15194 | — | < 1.15.4 | 1.15.4 | Sep 25, 2020 | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_ma | ||
| CVE-2020-15195 | — | < 1.15.4 | 1.15.4 | Sep 25, 2020 | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overfl | ||
| CVE-2020-15196 | — | >= 2.3.0, < 2.3.1 | 2.3.1 | Sep 25, 2020 | In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse a | ||
| CVE-2020-15197 | — | >= 2.3.0, < 2.3.1 | 2.3.1 | Sep 25, 2020 | In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes it | ||
| CVE-2020-15198 | — | >= 2.3.0, < 2.3.1 | 2.3.1 | Sep 25, 2020 | In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tens | ||
| CVE-2020-15199 | — | >= 2.3.0, < 2.3.1 | 2.3.1 | Sep 25, 2020 | In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this quantity to initiali | ||
| CVE-2020-15200 | — | >= 2.3.0, < 2.3.1 | 2.3.1 | Sep 25, 2020 | In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tenso | ||
| CVE-2020-15190 | — | < 1.15.4 | 1.15.4 | Sep 25, 2020 | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an | ||
| CVE-2018-21233 | — | < 1.7.0 | 1.7.0 | May 4, 2020 | TensorFlow before 1.7.0 has an integer overflow that causes an out-of-bounds read, possibly causing disclosure of the contents of process memory. This occurs in the DecodeBmp feature of the BMP decoder in core/kernels/decode_bmp_op.cc. | ||
| CVE-2020-5215 | — | < 1.15.2 | 1.15.2 | Jan 28, 2020 | In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a m | ||
| CVE-2019-16778 | — | < 1.15.0 | 1.15.0 | Dec 16, 2019 | In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of boun | ||
| CVE-2018-7575 | — | >= 1.0.0, < 1.7.1 | 1.7.1 | Apr 24, 2019 | Google TensorFlow 1.7.x and earlier is affected by a Buffer Overflow vulnerability. The type of exploitation is context-dependent. | ||
| CVE-2019-9635 | — | >= 1.0.0, < 1.12.1 | 1.12.1 | Apr 24, 2019 | NULL pointer dereference in Google TensorFlow before 1.12.2 could cause a denial of service via an invalid GIF file. | ||
| CVE-2018-10055 | — | >= 1.1.0, < 1.7.1 | 1.7.1 | Apr 24, 2019 | Invalid memory access and/or a heap buffer overflow in the TensorFlow XLA compiler in Google TensorFlow before 1.7.1 could cause a crash or read from other parts of process memory via a crafted configuration file. | ||
| CVE-2018-7577 | — | >= 1.1.0, < 1.7.1 | 1.7.1 | Apr 24, 2019 | Memcpy parameter overlap in Google Snappy library 1.1.4, as used in Google TensorFlow before 1.7.1, could result in a crash or read from other parts of process memory. | ||
| CVE-2018-8825 | — | >= 1.5.0, < 1.7.1 | 1.7.1 | Apr 23, 2019 | Google TensorFlow 1.7 and below is affected by: Buffer Overflow. The impact is: execute arbitrary code (local). |
- CVE-2020-15211Sep 25, 2020affected < 1.15.4fixed 1.15.4
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer f
- CVE-2020-15191Sep 25, 2020affected >= 2.2.0, < 2.2.1fixed 2.2.1
In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argument is not properly c
- CVE-2020-15192Sep 25, 2020affected >= 2.2.0, < 2.2.1fixed 2.2.1
In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly checked. Since each o
- CVE-2020-15193Sep 25, 2020affected >= 2.2.0, < 2.2.1fixed 2.2.1
In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping
- CVE-2020-15194Sep 25, 2020affected < 1.15.4fixed 1.15.4
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_ma
- CVE-2020-15195Sep 25, 2020affected < 1.15.4fixed 1.15.4
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overfl
- CVE-2020-15196Sep 25, 2020affected >= 2.3.0, < 2.3.1fixed 2.3.1
In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse a
- CVE-2020-15197Sep 25, 2020affected >= 2.3.0, < 2.3.1fixed 2.3.1
In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes it
- CVE-2020-15198Sep 25, 2020affected >= 2.3.0, < 2.3.1fixed 2.3.1
In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tens
- CVE-2020-15199Sep 25, 2020affected >= 2.3.0, < 2.3.1fixed 2.3.1
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this quantity to initiali
- CVE-2020-15200Sep 25, 2020affected >= 2.3.0, < 2.3.1fixed 2.3.1
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tenso
- CVE-2020-15190Sep 25, 2020affected < 1.15.4fixed 1.15.4
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an
- CVE-2018-21233May 4, 2020affected < 1.7.0fixed 1.7.0
TensorFlow before 1.7.0 has an integer overflow that causes an out-of-bounds read, possibly causing disclosure of the contents of process memory. This occurs in the DecodeBmp feature of the BMP decoder in core/kernels/decode_bmp_op.cc.
- CVE-2020-5215Jan 28, 2020affected < 1.15.2fixed 1.15.2
In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a m
- CVE-2019-16778Dec 16, 2019affected < 1.15.0fixed 1.15.0
In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of boun
- CVE-2018-7575Apr 24, 2019affected >= 1.0.0, < 1.7.1fixed 1.7.1
Google TensorFlow 1.7.x and earlier is affected by a Buffer Overflow vulnerability. The type of exploitation is context-dependent.
- CVE-2019-9635Apr 24, 2019affected >= 1.0.0, < 1.12.1fixed 1.12.1
NULL pointer dereference in Google TensorFlow before 1.12.2 could cause a denial of service via an invalid GIF file.
- CVE-2018-10055Apr 24, 2019affected >= 1.1.0, < 1.7.1fixed 1.7.1
Invalid memory access and/or a heap buffer overflow in the TensorFlow XLA compiler in Google TensorFlow before 1.7.1 could cause a crash or read from other parts of process memory via a crafted configuration file.
- CVE-2018-7577Apr 24, 2019affected >= 1.1.0, < 1.7.1fixed 1.7.1
Memcpy parameter overlap in Google Snappy library 1.1.4, as used in Google TensorFlow before 1.7.1, could result in a crash or read from other parts of process memory.
- CVE-2018-8825Apr 23, 2019affected >= 1.5.0, < 1.7.1fixed 1.7.1
Google TensorFlow 1.7 and below is affected by: Buffer Overflow. The impact is: execute arbitrary code (local).
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