Filtered by vendor Google
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Filtered by product Tensorflow
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Total
429 CVE
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2022-23592 | 1 Google | 1 Tensorflow | 2024-11-13 | 8.1 High |
| Tensorflow is an Open Source Machine Learning Framework. TensorFlow's type inference can cause a heap out of bounds read as the bounds checking is done in a `DCHECK` (which is a no-op during production). An attacker can control the `input_idx` variable such that `ix` would be larger than the number of values in `node_t.args`. The fix will be included in TensorFlow 2.8.0. This is the only affected version. | ||||
| CVE-2021-41200 | 1 Google | 1 Tensorflow | 2024-11-13 | 5.5 Medium |
| TensorFlow is an open source platform for machine learning. In affected versions if `tf.summary.create_file_writer` is called with non-scalar arguments code crashes due to a `CHECK`-fail. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-37686 | 1 Google | 1 Tensorflow | 2024-11-13 | 5.5 Medium |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122). An attacker can craft a model such that `ellipsis_end_idx` is smaller than `i` (e.g., always negative). In this case, the inner loop does not increase `i` and the `continue` statement causes execution to skip over the preincrement at the end of the outer loop. We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. TensorFlow 2.6.0 is the only affected version. | ||||
| CVE-2021-29544 | 1 Google | 1 Tensorflow | 2024-10-31 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`. This is because the implementation does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`. However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version. | ||||
| CVE-2023-33976 | 1 Google | 1 Tensorflow | 2024-10-01 | 7.5 High |
| TensorFlow is an end-to-end open source platform for machine learning. `array_ops.upper_bound` causes a segfault when not given a rank 2 tensor. The fix will be included in TensorFlow 2.13 and will also cherrypick this commit on TensorFlow 2.12. | ||||
| CVE-2018-21233 | 1 Google | 1 Tensorflow | 2024-08-05 | 6.5 Medium |
| 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-2018-10055 | 1 Google | 1 Tensorflow | 2024-08-05 | N/A |
| 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-2018-8825 | 1 Google | 1 Tensorflow | 2024-08-05 | N/A |
| Google TensorFlow 1.7 and below is affected by: Buffer Overflow. The impact is: execute arbitrary code (local). | ||||
| CVE-2018-7577 | 1 Google | 2 Snappy, Tensorflow | 2024-08-05 | N/A |
| 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-2018-7575 | 1 Google | 1 Tensorflow | 2024-08-05 | N/A |
| Google TensorFlow 1.7.x and earlier is affected by a Buffer Overflow vulnerability. The type of exploitation is context-dependent. | ||||
| CVE-2018-7576 | 1 Google | 1 Tensorflow | 2024-08-05 | N/A |
| Google TensorFlow 1.6.x and earlier is affected by: Null Pointer Dereference. The type of exploitation is: context-dependent. | ||||
| CVE-2019-16778 | 1 Google | 1 Tensorflow | 2024-08-05 | 2.6 Low |
| 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 bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0. | ||||
| CVE-2019-9635 | 1 Google | 1 Tensorflow | 2024-08-04 | N/A |
| NULL pointer dereference in Google TensorFlow before 1.12.2 could cause a denial of service via an invalid GIF file. | ||||
| CVE-2020-26266 | 1 Google | 1 Tensorflow | 2024-08-04 | 4.4 Medium |
| In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | ||||
| CVE-2020-26267 | 1 Google | 1 Tensorflow | 2024-08-04 | 4.4 Medium |
| In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | ||||
| CVE-2020-26271 | 1 Google | 1 Tensorflow | 2024-08-04 | 4.4 Medium |
| In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | ||||
| CVE-2020-26269 | 1 Google | 1 Tensorflow | 2024-08-04 | 7.5 High |
| In TensorFlow release candidate versions 2.4.0rc*, the general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories. There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these. This is patched in version 2.4.0. This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched. | ||||
| CVE-2020-26270 | 1 Google | 1 Tensorflow | 2024-08-04 | 4.4 Medium |
| In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | ||||
| CVE-2020-26268 | 1 Google | 1 Tensorflow | 2024-08-04 | 4.4 Medium |
| In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | ||||
| CVE-2020-15265 | 1 Google | 1 Tensorflow | 2024-08-04 | 5.9 Medium |
| In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. | ||||