Total
152 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2021-38423 | 1 Gurum | 1 Gurumdds | 2024-11-21 | 7.5 HIGH | 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-2021-35134 | 1 Qualcomm | 59 Qca6391, Qca6391 Firmware, Qcm6490 and 56 more | 2024-11-21 | N/A | 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 | 2024-11-21 | 4.6 MEDIUM | 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-2021-29545 | 1 Google | 1 Tensorflow | 2024-11-21 | 2.1 LOW | 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-2021-29542 | 1 Google | 1 Tensorflow | 2024-11-21 | 2.1 LOW | 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-2021-29537 | 1 Google | 1 Tensorflow | 2024-11-21 | 4.6 MEDIUM | 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 | 2024-11-21 | 4.6 MEDIUM | 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-2021-29535 | 1 Google | 1 Tensorflow | 2024-11-21 | 4.6 MEDIUM | 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-2021-29529 | 1 Google | 1 Tensorflow | 2024-11-21 | 4.6 MEDIUM | 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-2021-29521 | 1 Google | 1 Tensorflow | 2024-11-21 | 2.1 LOW | 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-2021-28039 | 3 Linux, Netapp, Xen | 4 Linux Kernel, Cloud Backup, Solidfire Baseboard Management Controller Firmware and 1 more | 2024-11-21 | 2.1 LOW | 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 | 2024-11-21 | 7.5 HIGH | 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-2021-22415 | 1 Huawei | 2 Emui, Magic Ui | 2024-11-21 | 5.0 MEDIUM | 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 | 2024-11-21 | 5.0 MEDIUM | 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. | |||||
| CVE-2021-22391 | 1 Huawei | 2 Emui, Magic Ui | 2024-11-21 | 5.0 MEDIUM | 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-2021-21824 | 1 Accusoft | 1 Imagegear | 2024-11-21 | 7.5 HIGH | 9.8 CRITICAL |
| An out-of-bounds write vulnerability exists in the JPG Handle_JPEG420 functionality of Accusoft ImageGear 19.9. A specially crafted malformed file can lead to memory corruption. An attacker can provide a malicious file to trigger this vulnerability. | |||||
| CVE-2021-21793 | 1 Accusoft | 1 Imagegear | 2024-11-21 | 6.8 MEDIUM | 8.8 HIGH |
| An out-of-bounds write vulnerability exists in the JPG sof_nb_comp header processing functionality of Accusoft ImageGear 19.8 and 19.9. A specially crafted malformed file can lead to memory corruption. An attacker can provide a malicious file to trigger this vulnerability. | |||||
| CVE-2021-21782 | 1 Accusoft | 1 Imagegear | 2024-11-21 | 6.8 MEDIUM | 8.8 HIGH |
| An out-of-bounds write vulnerability exists in the SGI format buffer size processing functionality of Accusoft ImageGear 19.8. A specially crafted malformed file can lead to memory corruption. An attacker can provide a malicious file to trigger this vulnerability. | |||||
| CVE-2021-21776 | 1 Accusoft | 1 Imagegear | 2024-11-21 | 6.8 MEDIUM | 8.8 HIGH |
| An out-of-bounds write vulnerability exists in the SGI Format Buffer Size Processing functionality of Accusoft ImageGear 19.8. A specially crafted malformed file can lead to memory corruption. An attacker can provide a malicious file to trigger this vulnerability. | |||||
| CVE-2021-21773 | 1 Accusoft | 1 Imagegear | 2024-11-21 | 6.8 MEDIUM | 7.8 HIGH |
| An out-of-bounds write vulnerability exists in the TIFF header count-processing functionality of Accusoft ImageGear 19.8. A specially crafted malformed file can lead to memory corruption. An attacker can provide a malicious file to trigger this vulnerability. | |||||