Total
185 CVE
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2021-3711 | 6 Debian, Netapp, Openssl and 3 more | 32 Debian Linux, Active Iq Unified Manager, Clustered Data Ontap and 29 more | 2024-11-21 | 9.8 Critical |
| In order to decrypt SM2 encrypted data an application is expected to call the API function EVP_PKEY_decrypt(). Typically an application will call this function twice. The first time, on entry, the "out" parameter can be NULL and, on exit, the "outlen" parameter is populated with the buffer size required to hold the decrypted plaintext. The application can then allocate a sufficiently sized buffer and call EVP_PKEY_decrypt() again, but this time passing a non-NULL value for the "out" parameter. A bug in the implementation of the SM2 decryption code means that the calculation of the buffer size required to hold the plaintext returned by the first call to EVP_PKEY_decrypt() can be smaller than the actual size required by the second call. This can lead to a buffer overflow when EVP_PKEY_decrypt() is called by the application a second time with a buffer that is too small. A malicious attacker who is able present SM2 content for decryption to an application could cause attacker chosen data to overflow the buffer by up to a maximum of 62 bytes altering the contents of other data held after the buffer, possibly changing application behaviour or causing the application to crash. The location of the buffer is application dependent but is typically heap allocated. Fixed in OpenSSL 1.1.1l (Affected 1.1.1-1.1.1k). | ||||
| CVE-2021-3491 | 2 Canonical, Linux | 2 Ubuntu Linux, Linux Kernel | 2024-11-21 | 7.8 High |
| The io_uring subsystem in the Linux kernel allowed the MAX_RW_COUNT limit to be bypassed in the PROVIDE_BUFFERS operation, which led to negative values being usedin mem_rw when reading /proc/<PID>/mem. This could be used to create a heap overflow leading to arbitrary code execution in the kernel. It was addressed via commit d1f82808877b ("io_uring: truncate lengths larger than MAX_RW_COUNT on provide buffers") (v5.13-rc1) and backported to the stable kernels in v5.12.4, v5.11.21, and v5.10.37. It was introduced in ddf0322db79c ("io_uring: add IORING_OP_PROVIDE_BUFFERS") (v5.7-rc1). | ||||
| CVE-2021-38423 | 1 Gurum | 1 Gurumdds | 2024-11-21 | 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-2021-37404 | 1 Apache | 1 Hadoop | 2024-11-21 | 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 | 2024-11-21 | 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 | 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 | 2.5 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 | 2.5 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 | 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 | 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 | 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 | 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 | 2.5 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 | 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 | 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-2021-25216 | 4 Debian, Isc, Netapp and 1 more | 23 Debian Linux, Bind, Active Iq Unified Manager and 20 more | 2024-11-21 | 8.1 High |
| In BIND 9.5.0 -> 9.11.29, 9.12.0 -> 9.16.13, and versions BIND 9.11.3-S1 -> 9.11.29-S1 and 9.16.8-S1 -> 9.16.13-S1 of BIND Supported Preview Edition, as well as release versions 9.17.0 -> 9.17.1 of the BIND 9.17 development branch, BIND servers are vulnerable if they are running an affected version and are configured to use GSS-TSIG features. In a configuration which uses BIND's default settings the vulnerable code path is not exposed, but a server can be rendered vulnerable by explicitly setting values for the tkey-gssapi-keytab or tkey-gssapi-credential configuration options. Although the default configuration is not vulnerable, GSS-TSIG is frequently used in networks where BIND is integrated with Samba, as well as in mixed-server environments that combine BIND servers with Active Directory domain controllers. For servers that meet these conditions, the ISC SPNEGO implementation is vulnerable to various attacks, depending on the CPU architecture for which BIND was built: For named binaries compiled for 64-bit platforms, this flaw can be used to trigger a buffer over-read, leading to a server crash. For named binaries compiled for 32-bit platforms, this flaw can be used to trigger a server crash due to a buffer overflow and possibly also to achieve remote code execution. We have determined that standard SPNEGO implementations are available in the MIT and Heimdal Kerberos libraries, which support a broad range of operating systems, rendering the ISC implementation unnecessary and obsolete. Therefore, to reduce the attack surface for BIND users, we will be removing the ISC SPNEGO implementation in the April releases of BIND 9.11 and 9.16 (it had already been dropped from BIND 9.17). We would not normally remove something from a stable ESV (Extended Support Version) of BIND, but since system libraries can replace the ISC SPNEGO implementation, we have made an exception in this case for reasons of stability and security. | ||||
| CVE-2021-22415 | 1 Huawei | 2 Emui, Magic Ui | 2024-11-21 | 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 | 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 | 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 | 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. | ||||