In isp, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07340373; Issue ID: ALPS07340373.
Das U-Boot from v2020.10 to v2022.07-rc3 was discovered to contain an out-of-bounds write via the function sqfs_readdir().
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.
A flaw was found in grub2. When reading a symbolic link's name from a UFS filesystem, grub2 fails to validate the string length taken as an input. The lack of validation may lead to a heap out-of-bounds write, causing data integrity issues and eventually allowing an attacker to circumvent secure boot protections.
TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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.
An issue was discovered in mgetty before 1.2.1. In contrib/next-login/login.c, the command-line parameter username is passed unsanitized to strcpy(), which can cause a stack-based buffer overflow.
A flaw was found in grub2. A specially crafted JPEG file can cause the JPEG parser of grub2 to incorrectly check the bounds of its internal buffers, resulting in an out-of-bounds write. The possibility of overwriting sensitive information to bypass secure boot protections is not discarded.
In widevine, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07446207; Issue ID: ALPS07446207.
A flaw was found in grub2. When reading tar files, grub2 allocates an internal buffer for the file name. However, it fails to properly verify the allocation against possible integer overflows. It's possible to cause the allocation length to overflow with a crafted tar file, leading to a heap out-of-bounds write. This flaw eventually allows an attacker to circumvent secure boot protections.
TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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.
When reading the language .mo file in grub_mofile_open(), grub2 fails to verify an integer overflow when allocating its internal buffer. A crafted .mo file may lead the buffer size calculation to overflow, leading to out-of-bound reads and writes. This flaw allows an attacker to leak sensitive data or overwrite critical data, possibly circumventing secure boot protections.
Possible memory corruption due to improper validation of memory address while processing user-space IOCTL for clearing Filter and Route statistics in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables
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.
In rpmb, there is a possible out of bounds write due to a logic error. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07460390; Issue ID: ALPS07460390.
In isp, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07213898; Issue ID: ALPS07213898.
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. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. 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. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. 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.
Stack-based buffer overflow in the gps_tracker function in airodump-ng.c in Aircrack-ng before 1.2 RC 1 allows local users to execute arbitrary code or gain privileges via unspecified vectors.
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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.
In phNxpNciHal_core_initialized of phNxpNciHal.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-231445184
In display, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07326239; Issue ID: ALPS07326239.
In display, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07326216; Issue ID: ALPS07326216.
In isp, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07310774; Issue ID: ALPS07310774.
In widevine, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07446228; Issue ID: ALPS07446228.
In WLAN driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06704526; Issue ID: ALPS06704393.
In mdp, there is a possible out of bounds write due to incorrect error handling. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07342114; Issue ID: ALPS07342114.
Memory corruption in Linux while sending DRM request.
In meta wifi, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07441652; Issue ID: ALPS07441652.
In keyinstall, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07439659; Issue ID: ALPS07439659.
In widevine, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07446213; Issue ID: ALPS07446213.
In Wi-Fi, there is a possible out of bounds write due to improper input validation. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07441630; Issue ID: ALPS07441630.
In isp, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07310780; Issue ID: ALPS07310780.
In gpu drm, there is a possible stack overflow due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07363501; Issue ID: ALPS07363501.
In keyinstall, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07510064; Issue ID: ALPS07510064.
In cpu dvfs, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07139405; Issue ID: ALPS07139405.
In widevine, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07446228; Issue ID: ALPS07446228.
NVIDIA DGX A100 contains a vulnerability in SBIOS in the SmbiosPei, which may allow a highly privileged local attacker to cause an out-of-bounds write, which may lead to code execution, denial of service, compromised integrity, and information disclosure.
Out-Of-Bounds Write in TPM2 Reference Library in Google ChromeOS 15753.50.0 stable on Cr50 Boards allows an attacker with root access to gain persistence and Bypass operating system verification via exploiting the NV_Read functionality during the Challenge-Response process.
NVIDIA DGX A100 contains a vulnerability in SBIOS in the IpSecDxe, where a user with elevated privileges and a preconditioned heap can exploit an out-of-bounds write vulnerability, which may lead to code execution, denial of service, data integrity impact, and information disclosure.
Win32k Elevation of Privilege Vulnerability
A heap overflow in LzmaUefiDecompressGetInfo function in EDK II.
A heap-based buffer overflow was found in the Linux kernel's LightNVM subsystem. The issue results from the lack of proper validation of the length of user-supplied data prior to copying it to a fixed-length heap-based buffer. This vulnerability allows a local attacker to escalate privileges and execute arbitrary code in the context of the kernel. The attacker must first obtain the ability to execute high-privileged code on the target system to exploit this vulnerability.
An issue was discovered on Samsung mobile devices with JBP(4.2) and KK(4.4) (Marvell chipsets) software. The ACIPC-MSOCKET driver allows local privilege escalation via a stack-based buffer overflow. The Samsung ID is SVE-2016-5393 (April 2016).
A crafted NTFS image can cause a heap-based buffer overflow in ntfs_check_log_client_array in NTFS-3G through 2021.8.22.
A crafted NTFS image can cause a heap-based buffer overflow in ntfs_mft_rec_alloc in NTFS-3G through 2021.8.22.
An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_nan_subscribe_get_nl_params(), there is no input validation check on hal_req->num_intf_addr_present coming from userspace, which can lead to a heap overwrite.
An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_nan_config_get_nl_params(), there is no input validation check on hal_req->num_config_discovery_attr coming from userspace, which can lead to a heap overwrite.
FuzeZip 1.0.0.131625 has a Local Buffer Overflow vulnerability
An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_get_scan_extra_ies(), there is no input validation check on default_ies coming from userspace, which can lead to a heap overwrite.