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10294 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2016-4134 | 8 Adobe, Apple, Google and 5 more | 16 Flash Player, Flash Player Desktop Runtime, Macos and 13 more | 2021-11-19 | 9.3 HIGH | 8.8 HIGH |
| Unspecified vulnerability in Adobe Flash Player 21.0.0.242 and earlier, as used in the Adobe Flash libraries in Microsoft Internet Explorer 10 and 11 and Microsoft Edge, has unknown impact and attack vectors, a different vulnerability than other CVEs listed in MS16-083. | |||||
| CVE-2016-4133 | 8 Adobe, Apple, Google and 5 more | 16 Flash Player, Flash Player Desktop Runtime, Macos and 13 more | 2021-11-19 | 9.3 HIGH | 8.8 HIGH |
| Unspecified vulnerability in Adobe Flash Player 21.0.0.242 and earlier, as used in the Adobe Flash libraries in Microsoft Internet Explorer 10 and 11 and Microsoft Edge, has unknown impact and attack vectors, a different vulnerability than other CVEs listed in MS16-083. | |||||
| CVE-2016-4144 | 8 Adobe, Apple, Google and 5 more | 16 Flash Player, Flash Player Desktop Runtime, Macos and 13 more | 2021-11-19 | 9.3 HIGH | 8.8 HIGH |
| Unspecified vulnerability in Adobe Flash Player 21.0.0.242 and earlier, as used in the Adobe Flash libraries in Microsoft Internet Explorer 10 and 11 and Microsoft Edge, has unknown impact and attack vectors, a different vulnerability than other CVEs listed in MS16-083. | |||||
| CVE-2016-4131 | 8 Adobe, Apple, Google and 5 more | 16 Flash Player, Flash Player Desktop Runtime, Macos and 13 more | 2021-11-19 | 9.3 HIGH | 8.8 HIGH |
| Unspecified vulnerability in Adobe Flash Player 21.0.0.242 and earlier, as used in the Adobe Flash libraries in Microsoft Internet Explorer 10 and 11 and Microsoft Edge, has unknown impact and attack vectors, a different vulnerability than other CVEs listed in MS16-083. | |||||
| CVE-2016-4132 | 8 Adobe, Apple, Google and 5 more | 16 Flash Player, Flash Player Desktop Runtime, Macos and 13 more | 2021-11-19 | 9.3 HIGH | 8.8 HIGH |
| Unspecified vulnerability in Adobe Flash Player 21.0.0.242 and earlier, as used in the Adobe Flash libraries in Microsoft Internet Explorer 10 and 11 and Microsoft Edge, has unknown impact and attack vectors, a different vulnerability than other CVEs listed in MS16-083. | |||||
| CVE-2016-4146 | 8 Adobe, Apple, Google and 5 more | 16 Flash Player, Flash Player Desktop Runtime, Macos and 13 more | 2021-11-19 | 9.3 HIGH | 8.8 HIGH |
| Unspecified vulnerability in Adobe Flash Player 21.0.0.242 and earlier, as used in the Adobe Flash libraries in Microsoft Internet Explorer 10 and 11 and Microsoft Edge, has unknown impact and attack vectors, a different vulnerability than other CVEs listed in MS16-083. | |||||
| CVE-2020-15213 | 1 Google | 1 Tensorflow | 2021-11-18 | 4.3 MEDIUM | 4.0 MEDIUM |
| In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | |||||
| CVE-2020-15210 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 5.8 MEDIUM | 6.5 MEDIUM |
| In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | |||||
| CVE-2020-15207 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 6.8 MEDIUM | 9.0 CRITICAL |
| In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | |||||
| CVE-2020-15205 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 7.5 HIGH | 9.8 CRITICAL |
| In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | |||||
| CVE-2020-15203 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 5.0 MEDIUM | 7.5 HIGH |
| In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | |||||
| CVE-2020-15202 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 6.8 MEDIUM | 9.0 CRITICAL |
| In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | |||||
| CVE-2020-15200 | 1 Google | 1 Tensorflow | 2021-11-18 | 4.3 MEDIUM | 5.9 MEDIUM |
| 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` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | |||||
| CVE-2020-15201 | 1 Google | 1 Tensorflow | 2021-11-18 | 6.8 MEDIUM | 4.8 MEDIUM |
| 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` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | |||||
| CVE-2020-15198 | 1 Google | 1 Tensorflow | 2021-11-18 | 5.8 MEDIUM | 5.4 MEDIUM |
| 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 tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | |||||
| CVE-2020-15199 | 1 Google | 1 Tensorflow | 2021-11-18 | 4.3 MEDIUM | 5.9 MEDIUM |
| 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 initialize a different data structure. Since `BatchedMap` is equivalent to a vector, it needs to have at least one element to not be `nullptr`. If user passes a `splits` tensor that is empty or has exactly one element, we get a `SIGABRT` signal raised by the operating system. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | |||||
| CVE-2020-15196 | 1 Google | 1 Tensorflow | 2021-11-18 | 6.5 MEDIUM | 9.9 CRITICAL |
| 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 and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | |||||
| CVE-2020-15193 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 5.5 MEDIUM | 7.1 HIGH |
| 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 users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | |||||
| CVE-2020-15195 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 6.5 MEDIUM | 8.8 HIGH |
| 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 overflow. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | |||||
| CVE-2020-15190 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 5.0 MEDIUM | 5.3 MEDIUM |
| 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 empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. In this case, this results in a segmentation fault The issue is patched in commit da8558533d925694483d2c136a9220d6d49d843c, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | |||||