| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Possible buffer overflow while printing the HARQ memory partition detail due to improper validation of buffer size in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile |
| Possible buffer overflow due to lack of buffer length check when segmented WMI command is received in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking |
| Possible out of bound access due to improper validation of item size and DIAG memory pools data while switching between USB and PCIE interface in Snapdragon Auto, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking |
| Possible out of bound read due to improper validation of packet length while handling data transfer in VR service in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Wearables |
| Possible heap overflow due to improper validation of local variable while storing current task information locally in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables |
| Possible heap Memory Corruption Issue due to lack of input validation when sending HWTC IQ Capture command in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables |
| FFmpeg <=4.3 contains a buffer overflow vulnerability in libavcodec through a crafted file that may lead to remote code execution. |
| SerenityOS 2021-03-27 contains a buffer overflow vulnerability in the EndOfCentralDirectory::read() function. |
| Mozilla developers reported memory safety bugs present in Firefox 90 and Firefox ESR 78.12. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code. This vulnerability affects Thunderbird < 78.13, Firefox ESR < 78.13, and Firefox < 91. |
| Mozilla developers reported memory safety bugs present in code shared between Firefox and Thunderbird. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code. This vulnerability affects Thunderbird < 78.12, Firefox ESR < 78.12, and Firefox < 90. |
| Mozilla developers reported memory safety bugs present in Firefox 88 and Firefox ESR 78.11. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code. This vulnerability affects Thunderbird < 78.11, Firefox < 89, and Firefox ESR < 78.11. |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. 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. |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. 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. |
| TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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. |
| TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. 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, as these are also affected. |
| Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. A heap buffer overflow was found in Exiv2 versions v0.27.3 and earlier. The heap overflow is triggered when Exiv2 is used to write metadata into a crafted image file. An attacker could potentially exploit the vulnerability to gain code execution, 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 v0.27.4. |
| TP-Link TL-WR802N(US), Archer_C50v5_US v4_200 <= 2020.06 contains a buffer overflow vulnerability in the httpd process in the body message. The attack vector is: The attacker can get shell of the router by sending a message through the network, which may lead to remote code execution. |
| Buffer Overflow in Emerson GE Automation Proficy Machine Edition v8.0 allows an attacker to cause a denial of service and application crash via crafted traffic from a Man-in-the-Middle (MITM) attack to the component "FrameworX.exe" in the module "MSVCR100.dll". |
| Multiple buffer overflow security vulnerabilities have been identified in HPE iLO Amplifier Pack version(s): Prior to 2.12. These vulnerabilities could be exploited by a highly privileged user to remotely execute code that could lead to a loss of confidentiality, integrity, and availability. HPE has provided a software update to resolve this vulnerability in HPE iLO Amplifier Pack. |
| A potential local buffer overflow vulnerability has been identified in HPE FlexNetwork 5130 EL Switch Series version: Prior to 5130_EI_7.10.R3507P02. HPE has made the following software update to resolve the vulnerability in HPE FlexNetwork 5130 EL Switch Series version 5130_EL_7.10.R3507P02. |
