Total
7208 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2021-37685 | 1 Google | 1 Tensorflow | 2024-11-21 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`expand_dims.cc`](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If `axis` is a large negative value (e.g., `-100000`), then after the first `if` it would still be negative. The check following the `if` statement will pass and the `for` loop would read one element before the start of `input_dims.data` (when `i = 0`). We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37672 | 1 Google | 1 Tensorflow | 2024-11-21 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples. We have patched the issue in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37670 | 1 Google | 1 Tensorflow | 2024-11-21 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.UpperBound`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of `sorted_input` argument. A similar issue occurs in `tf.raw_ops.LowerBound`. We have patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37664 | 1 Google | 1 Tensorflow | 2024-11-21 | 3.6 LOW | 7.3 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37659 | 1 Google | 1 Tensorflow | 2024-11-21 | 4.6 MEDIUM | 7.3 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37655 | 1 Google | 1 Tensorflow | 2024-11-21 | 4.6 MEDIUM | 7.3 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37654 | 1 Google | 1 Tensorflow | 2024-11-21 | 3.6 LOW | 7.3 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a `CHECK`-fail in debug builds of TensorFlow using `tf.raw_ops.ResourceGather` or a read from outside the bounds of heap allocated data in the same API in a release build. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the `batch_dims` value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of `tensor`, this results in reading data from outside the bounds of heap allocated buffer backing the tensor. We have patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37641 | 1 Google | 1 Tensorflow | 2024-11-21 | 3.6 LOW | 7.3 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37639 | 1 Google | 1 Tensorflow | 2024-11-21 | 4.6 MEDIUM | 8.4 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37635 | 1 Google | 1 Tensorflow | 2024-11-21 | 3.6 LOW | 7.3 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37620 | 3 Debian, Exiv2, Fedoraproject | 3 Debian Linux, Exiv2, Fedora | 2024-11-21 | 4.3 MEDIUM | 4.7 MEDIUM |
| Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. An out-of-bounds read was found in Exiv2 versions v0.27.4 and earlier. The out-of-bounds read is triggered when Exiv2 is used to read the metadata of a crafted image file. An attacker could potentially exploit the vulnerability to cause a denial of service, if they can trick the victim into running Exiv2 on a crafted image file. The bug is fixed in version v0.27.5. | |||||
| CVE-2021-37619 | 2 Exiv2, Fedoraproject | 2 Exiv2, Fedora | 2024-11-21 | 4.3 MEDIUM | 4.7 MEDIUM |
| Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. An out-of-bounds read was found in Exiv2 versions v0.27.4 and earlier. The out-of-bounds read is triggered when Exiv2 is used to write metadata into a crafted image file. An attacker could potentially exploit the vulnerability to cause a denial of service by crashing Exiv2, if they can trick the victim into running Exiv2 on a crafted image file. Note that this bug is only triggered when writing the metadata, which is a less frequently used Exiv2 operation than reading the metadata. For example, to trigger the bug in the Exiv2 command-line application, you need to add an extra command-line argument such as insert. The bug is fixed in version v0.27.5. | |||||
| CVE-2021-37618 | 2 Exiv2, Fedoraproject | 2 Exiv2, Fedora | 2024-11-21 | 4.3 MEDIUM | 4.7 MEDIUM |
| Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. An out-of-bounds read was found in Exiv2 versions v0.27.4 and earlier. The out-of-bounds read is triggered when Exiv2 is used to print the metadata of a crafted image file. An attacker could potentially exploit the vulnerability to cause a denial of service, if they can trick the victim into running Exiv2 on a crafted image file. Note that this bug is only triggered when printing the image ICC profile, which is a less frequently used Exiv2 operation that requires an extra command line option (`-p C`). The bug is fixed in version v0.27.5. | |||||
| CVE-2021-37570 | 1 Mediatek | 14 Mt7603e, Mt7603e Firmware, Mt7613 and 11 more | 2024-11-21 | 5.0 MEDIUM | 8.2 HIGH |
| MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle IEEE 1905 protocols. (Affected Chipsets MT7603E, MT7613, MT7615, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 2.0.2; Out-of-bounds read). | |||||
| CVE-2021-37567 | 1 Mediatek | 14 Mt7603e, Mt7603e Firmware, Mt7613 and 11 more | 2024-11-21 | 5.0 MEDIUM | 8.2 HIGH |
| MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle IEEE 1905 protocols. (Affected Chipsets MT7603E, MT7613, MT7615, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 2.0.2; Out-of-bounds read). | |||||
| CVE-2021-37565 | 1 Mediatek | 14 Mt7603e, Mt7603e Firmware, Mt7613 and 11 more | 2024-11-21 | 5.0 MEDIUM | 8.2 HIGH |
| MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle IEEE 1905 protocols. (Affected Chipsets MT7603E, MT7613, MT7615, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 2.0.2; Out-of-bounds read). | |||||
| CVE-2021-37564 | 1 Mediatek | 14 Mt7603e, Mt7603e Firmware, Mt7613 and 11 more | 2024-11-21 | 5.0 MEDIUM | 8.2 HIGH |
| MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle IEEE 1905 protocols. (Affected Chipsets MT7603E, MT7613, MT7615, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 2.0.2; Out-of-bounds read). | |||||
| CVE-2021-37562 | 1 Mediatek | 20 Mt7603e, Mt7603e Firmware, Mt7610 and 17 more | 2024-11-21 | 5.0 MEDIUM | 8.2 HIGH |
| MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle the WPS (Wi-Fi Protected Setup) protocol. (Affected Chipsets MT7603E, MT7610, MT7612, MT7613, MT7615, MT7620, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 7.4.0.0; Out-of-bounds read). | |||||
| CVE-2021-37203 | 1 Siemens | 2 Nx 1980, Solid Edge | 2024-11-21 | 5.8 MEDIUM | 7.1 HIGH |
| A vulnerability has been identified in NX 1980 Series (All versions < V1984), Solid Edge SE2021 (All versions < SE2021MP8). The plmxmlAdapterIFC.dll contains an out-of-bounds read while parsing user supplied IFC files which could result in a read past the end of an allocated buffer. This could allow an attacker to cause a denial-of-service condition or read sensitive information from memory locations. | |||||
| CVE-2021-37176 | 1 Siemens | 1 Simcenter Femap | 2024-11-21 | 4.3 MEDIUM | 3.3 LOW |
| A vulnerability has been identified in Simcenter Femap V2020.2 (All versions), Simcenter Femap V2021.1 (All versions). The femap.exe application lacks proper validation of user-supplied data when parsing modfem files. This could result in an out of bounds read past the end of an allocated buffer. An attacker could leverage this vulnerability to leak information in the context of the current process. (ZDI-CAN-14260) | |||||