Total
210374 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2021-38532 | 1 Netgear | 2 Wac104, Wac104 Firmware | 2021-08-19 | 6.5 MEDIUM | 7.2 HIGH |
| NETGEAR WAC104 devices before 1.0.4.15 are affected by incorrect configuration of security settings. | |||||
| CVE-2021-38533 | 1 Netgear | 2 Rax40, Rax40 Firmware | 2021-08-19 | 3.5 LOW | 5.4 MEDIUM |
| NETGEAR RAX40 devices before 1.0.3.64 are affected by stored XSS. | |||||
| CVE-2021-38522 | 1 Netgear | 2 R6400, R6400 Firmware | 2021-08-19 | 6.5 MEDIUM | 7.2 HIGH |
| NETGEAR R6400 devices before 1.0.1.52 are affected by a stack-based buffer overflow by an authenticated user. | |||||
| CVE-2021-38523 | 1 Netgear | 2 R6400, R6400 Firmware | 2021-08-19 | 6.5 MEDIUM | 7.2 HIGH |
| NETGEAR R6400 devices before 1.0.1.70 are affected by a stack-based buffer overflow by an authenticated user. | |||||
| CVE-2021-38524 | 1 Netgear | 26 Mk62, Mk62 Firmware, Mr60 and 23 more | 2021-08-19 | 4.0 MEDIUM | 4.9 MEDIUM |
| Certain NETGEAR devices are affected by a stack-based buffer overflow by an authenticated user. This affects MK62 before 1.0.6.110, MR60 before 1.0.6.110, MS60 before 1.0.6.110, RAX15 before 1.0.2.82, RAX20 before 1.0.2.82, RAX200 before 1.0.3.106, RAX45 before 1.0.2.32, RAX50 before 1.0.2.32, RAX75 before 1.0.3.106, RAX80 before 1.0.3.106, RBK752 before 3.2.16.6, RBR750 before 3.2.16.6, and RBS750 before 3.2.16.6. | |||||
| CVE-2021-38519 | 1 Netgear | 27 R6250, R6250 Firmware, R6300 and 24 more | 2021-08-19 | 6.5 MEDIUM | 7.2 HIGH |
| Certain NETGEAR devices are affected by command injection by an authenticated user. This affects R6250 before 1.0.4.36, R6300v2 before 1.0.4.36, R6400 before 1.0.1.50, R6400v2 before 1.0.2.66, R6700v3 before 1.0.2.66, R6700 before 1.0.2.8, R6900 before 1.0.2.8, R7000 before 1.0.9.88, R6900P before 1.3.2.132, R7100LG before 1.0.0.52, R7900 before 1.0.3.10, R8000 before 1.0.4.46, R7900P before 1.4.1.50, R8000P before 1.4.1.50, and RAX80 before 1.0.1.40. | |||||
| CVE-2021-38517 | 1 Netgear | 8 R6400, R6400 Firmware, Rax75 and 5 more | 2021-08-19 | 6.5 MEDIUM | 7.2 HIGH |
| Certain NETGEAR devices are affected by out-of-bounds reads and writes. This affects R6400 before 1.0.1.70, RAX75 before 1.0.4.120, RAX80 before 1.0.4.120, and XR300 before 1.0.3.50. | |||||
| CVE-2020-7537 | 1 Schneider-electric | 38 Bmxp341000, Bmxp341000 Firmware, Bmxp342000 and 35 more | 2021-08-18 | 5.0 MEDIUM | 7.5 HIGH |
| A CWE-754: Improper Check for Unusual or Exceptional Conditions vulnerability exists in Modicon M580, Modicon M340, Legacy Controllers Modicon Quantum & Modicon Premium (see security notifications for affected versions), that could cause denial of service when a specially crafted Read Physical Memory request over Modbus is sent to the controller. | |||||
| CVE-2020-7542 | 1 Schneider-electric | 40 140cpu65150, 140cpu65150 Firmware, Bmxp341000 and 37 more | 2021-08-18 | 5.0 MEDIUM | 7.5 HIGH |
| A CWE-754: Improper Check for Unusual or Exceptional Conditions vulnerability exists in Modicon M580, Modicon M340, Legacy Controllers Modicon Quantum & Modicon Premium (see security notifications for affected versions), that could cause denial of service when a specially crafted Read Physical Memory request over Modbus is sent to the controller. | |||||
| CVE-2020-7543 | 1 Schneider-electric | 32 Bmxp341000, Bmxp341000 Firmware, Bmxp342000 and 29 more | 2021-08-18 | 5.0 MEDIUM | 7.5 HIGH |
| A CWE-754: Improper Check for Unusual or Exceptional Conditions vulnerability exists in Modicon M580, Modicon M340, Legacy Controllers Modicon Quantum & Modicon Premium (see security notifications for affected versions), that could cause denial of service when a specially crafted Read Physical Memory request over Modbus is sent to the controller. | |||||
| CVE-2021-37685 | 1 Google | 1 Tensorflow | 2021-08-18 | 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-2021-37684 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0. We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695). 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-2021-37683 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of division in TFLite is [vulnerable to a division by 0 error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. We have patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28. 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-2021-37691 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). We have patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit 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-2021-37687 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. 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-2021-37688 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. 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-2021-37689 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. 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-2021-37658 | 1 Google | 1 Tensorflow | 2021-08-18 | 4.6 MEDIUM | 7.8 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 operations of type `tf.raw_ops.MatrixSetDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit ff8894044dfae5568ecbf2ed514c1a37dc394f1b. 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-2021-37661 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992. 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-2021-37662 | 1 Google | 1 Tensorflow | 2021-08-18 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in `BoostedTreesCalculateBestGainsPerFeature` and similar attack can occur in `BoostedTreesCalculateBestFeatureSplitV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. We have patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7. 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. | |||||