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11915 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| 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-2020-15201 | 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-2020-15200 | 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-2020-15199 | 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-2020-15198 | 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-2020-15196 | 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-2020-15195 | 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-2020-15193 | 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-2020-15192 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 4.0 MEDIUM | 4.3 MEDIUM |
| 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 of the above methods can return an error status, the `status` value must be checked before continuing. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | |||||
| CVE-2020-15191 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2021-11-18 | 5.0 MEDIUM | 5.3 MEDIUM |
| 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 checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 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. | |||||
| CVE-2020-15266 | 1 Google | 1 Tensorflow | 2021-11-18 | 5.0 MEDIUM | 7.5 HIGH |
| In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. 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. | |||||
| CVE-2016-5696 | 3 Google, Linux, Oracle | 3 Android, Linux Kernel, Vm Server | 2021-11-17 | 5.8 MEDIUM | 4.8 MEDIUM |
| net/ipv4/tcp_input.c in the Linux kernel before 4.7 does not properly determine the rate of challenge ACK segments, which makes it easier for remote attackers to hijack TCP sessions via a blind in-window attack. | |||||
| CVE-2008-5915 | 1 Google | 1 Chrome | 2021-11-15 | 2.1 LOW | N/A |
| An unspecified function in the JavaScript implementation in Google Chrome creates and exposes a "temporary footprint" when there is a current login to a web site, which makes it easier for remote attackers to trick a user into acting upon a spoofed pop-up message, aka an "in-session phishing attack." NOTE: as of 20090116, the only disclosure is a vague pre-advisory with no actionable information. However, because it is from a well-known researcher, it is being assigned a CVE identifier for tracking purposes. | |||||
| CVE-2009-1598 | 1 Google | 1 Chrome | 2021-11-15 | 9.3 HIGH | N/A |
| Google Chrome executes DOM calls in response to a javascript: URI in the target attribute of a submit element within a form contained in an inline PDF file, which might allow remote attackers to bypass intended Adobe Acrobat JavaScript restrictions on accessing the document object, as demonstrated by a web site that permits PDF uploads by untrusted users, and therefore has a shared document.domain between the web site and this javascript: URI. NOTE: the researcher reports that Adobe's position is "a PDF file is active content." | |||||
| CVE-2010-1731 | 2 Google, Htc | 2 Chrome, Hero | 2021-11-15 | 4.3 MEDIUM | N/A |
| Google Chrome on the HTC Hero allows remote attackers to cause a denial of service (application crash) via JavaScript that writes <marquee> sequences in an infinite loop. | |||||
| CVE-2021-43189 | 2 Google, Jetbrains | 2 Android, Youtrack Mobile | 2021-11-15 | 7.5 HIGH | 7.3 HIGH |
| In JetBrains YouTrack Mobile before 2021.2, access token protection on Android is incomplete. | |||||
| CVE-2014-0569 | 7 Adobe, Apple, Google and 4 more | 14 Air Desktop Runtime, Air Sdk, Flash Player and 11 more | 2021-11-10 | 9.3 HIGH | N/A |
| Integer overflow in Adobe Flash Player before 13.0.0.250 and 14.x and 15.x before 15.0.0.189 on Windows and OS X and before 11.2.202.411 on Linux, Adobe AIR before 15.0.0.293, Adobe AIR SDK before 15.0.0.302, and Adobe AIR SDK & Compiler before 15.0.0.302 allows attackers to execute arbitrary code via unspecified vectors. | |||||
| CVE-2014-0564 | 7 Adobe, Apple, Google and 4 more | 14 Air Desktop Runtime, Air Sdk, Flash Player and 11 more | 2021-11-10 | 10.0 HIGH | N/A |
| Adobe Flash Player before 13.0.0.250 and 14.x and 15.x before 15.0.0.189 on Windows and OS X and before 11.2.202.411 on Linux, Adobe AIR before 15.0.0.293, Adobe AIR SDK before 15.0.0.302, and Adobe AIR SDK & Compiler before 15.0.0.302 allow attackers to execute arbitrary code or cause a denial of service (memory corruption) via unspecified vectors, a different vulnerability than CVE-2014-0558. | |||||
| CVE-2021-41225 | 1 Google | 1 Tensorflow | 2021-11-10 | 2.1 LOW | 7.8 HIGH |
| TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a use of unitialized variable. If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized. 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. | |||||