Total
168 CVE
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2021-40526 | 1 Onepeloton | 2 Ttr01, Ttr01 Firmware | 2024-08-04 | 4.8 Medium |
| Incorrect calculation of buffer size vulnerability in Peleton TTR01 up to and including PTV55G allows a remote attacker to trigger a Denial of Service attack through the GymKit daemon process by exploiting a heap overflow in the network server handling the Apple GymKit communication. This can lead to an Apple MFI device not being able to authenticate with the Peleton Bike | ||||
| CVE-2021-40052 | 1 Huawei | 3 Emui, Harmonyos, Magic Ui | 2024-08-04 | 7.5 High |
| There is an incorrect buffer size calculation vulnerability in the video framework.Successful exploitation of this vulnerability may affect availability. | ||||
| CVE-2021-40048 | 1 Huawei | 3 Emui, Harmonyos, Magic Ui | 2024-08-04 | 7.5 High |
| There is an incorrect buffer size calculation vulnerability in the video framework. Successful exploitation of this vulnerability will affect availability. | ||||
| CVE-2021-38423 | 1 Gurum | 1 Gurumdds | 2024-08-04 | 6.6 Medium |
| All versions of GurumDDS improperly calculate the size to be used when allocating the buffer, which may result in a buffer overflow. | ||||
| CVE-2021-38435 | 1 Rti | 2 Connext Dds Professional, Connext Dds Secure | 2024-08-04 | 6.6 Medium |
| RTI Connext DDS Professional and Connext DDS Secure Versions 4.2x to 6.1.0 not correctly calculate the size when allocating the buffer, which may result in a buffer overflow. | ||||
| CVE-2021-37404 | 1 Apache | 1 Hadoop | 2024-08-04 | 9.8 Critical |
| There is a potential heap buffer overflow in Apache Hadoop libhdfs native code. Opening a file path provided by user without validation may result in a denial of service or arbitrary code execution. Users should upgrade to Apache Hadoop 2.10.2, 3.2.3, 3.3.2 or higher. | ||||
| CVE-2021-35134 | 1 Qualcomm | 59 Qca6391, Qca6391 Firmware, Qcm6490 and 56 more | 2024-08-04 | 8.4 High |
| Due to insufficient validation of ELF headers, an Incorrect Calculation of Buffer Size can occur in Boot leading to memory corruption in Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile | ||||
| CVE-2021-29608 | 1 Google | 1 Tensorflow | 2024-08-03 | 5.3 Medium |
| TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29521 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3. | ||||
| CVE-2021-29545 | 1 Google | 1 Tensorflow | 2024-08-03 | 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 converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29535 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29537 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29536 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29542 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements. If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29529 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-28039 | 3 Linux, Netapp, Xen | 4 Linux Kernel, Cloud Backup, Solidfire Baseboard Management Controller Firmware and 1 more | 2024-08-03 | 6.5 Medium |
| An issue was discovered in the Linux kernel 5.9.x through 5.11.3, as used with Xen. In some less-common configurations, an x86 PV guest OS user can crash a Dom0 or driver domain via a large amount of I/O activity. The issue relates to misuse of guest physical addresses when a configuration has CONFIG_XEN_UNPOPULATED_ALLOC but not CONFIG_XEN_BALLOON_MEMORY_HOTPLUG. | ||||
| CVE-2021-27378 | 1 Rand Core Project | 1 Rand Core | 2024-08-03 | 9.8 Critical |
| An issue was discovered in the rand_core crate before 0.6.2 for Rust. Because read_u32_into and read_u64_into mishandle certain buffer-length checks, a random number generator may be seeded with too little data. | ||||
| CVE-2021-22391 | 1 Huawei | 2 Emui, Magic Ui | 2024-08-03 | 7.5 High |
| There is an Incorrect Calculation of Buffer Size in Huawei Smartphone.Successful exploitation of this vulnerability may cause the system to reset. | ||||
| CVE-2021-22415 | 1 Huawei | 2 Emui, Magic Ui | 2024-08-03 | 7.5 High |
| There is an Incorrect Calculation of Buffer Size Vulnerability in Huawei Smartphone.Successful exploitation of this vulnerability may cause kernel exceptions with the code. | ||||
| CVE-2021-22392 | 1 Huawei | 2 Emui, Magic Ui | 2024-08-03 | 7.5 High |
| There is an Incorrect Calculation of Buffer Size in Huawei Smartphone.Successful exploitation of this vulnerability may cause verification bypass and directions to abnormal addresses. | ||||