TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Transpose` is vulnerable to a heap buffer overflow. This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

In copy_io_entries of lwis_ioctl.c, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-205992503References: N/A

In amcs_cdev_unlocked_ioctl of audiometrics.c, 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.Product: AndroidVersions: Android kernelAndroid ID: A-206128522References: N/A

In prepare_io_entry and prepare_response of lwis_ioctl.c and lwis_periodic_io.c, 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.Product: AndroidVersions: Android kernelAndroid ID: A-205995773References: N/A

In TBD of TBD, there is a possible out of bounds write due to memory corruption. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-195726151References: N/A

binutils version 2.32 and earlier contains a Integer Overflow vulnerability in objdump, bfd_get_dynamic_reloc_upper_bound,bfd_canonicalize_dynamic_reloc that can result in Integer overflow trigger heap overflow. Successful exploitation allows execution of arbitrary code.. This attack appear to be exploitable via Local. This vulnerability appears to have been fixed in after commit 3a551c7a1b80fca579461774860574eabfd7f18f.

In ProtocolStkProactiveCommandAdapter::Init of protocolstkadapter.cpp, 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.Product: AndroidVersions: Android kernelAndroid ID: A-205035540References: N/A

Stack-based buffer overflow in the m2m1shot_compat_ioctl32 function in the Samsung m2m1shot driver framework, as used in Samsung S6 Edge, allows local users to have unspecified impact via a large data.buf_out.num_planes value in an ioctl call.

In ProtocolStkProactiveCommandAdapter::Init of protocolstkadapter.cpp, 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.Product: AndroidVersions: Android kernelAndroid ID: A-205036834References: N/A

In sec_ts_parsing_cmds of (TBD), 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.Product: AndroidVersions: Android kernelAndroid ID: A-194499021References: N/A

In Keymaster, 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-12LAndroid ID: A-173567719

A vulnerability has been found in code-projects Theater Seat Booking System 1.0 and classified as critical. Affected by this vulnerability is the function cancel. The manipulation of the argument cancelcustomername leads to stack-based buffer overflow. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used.

A vulnerability has been found in Nsasoft Product Key Explorer 4.0.9 and classified as problematic. Affected by this vulnerability is an unknown functionality of the component Registration Handler. The manipulation of the argument Name/Key leads to memory corruption. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. The associated identifier of this vulnerability is VDB-251671. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.

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.

IBM i2 Analyst's Notebook 9.2.0, 9.2.1, and 9.2.2 is vulnerable to a stack-based buffer overflow, caused by improper bounds checking. A local attacker could overflow a buffer and gain lower level privileges. IBM X-Force ID: 214440.

A vulnerability was found in Nsasoft ShareAlarmPro 2.1.4 and classified as problematic. Affected by this issue is some unknown functionality of the component Registration Handler. The manipulation of the argument Name/Key leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used. The identifier of this vulnerability is VDB-251672. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.

RTI Connext DDS Professional and Connext DDS Secure Versions 4.2.x to 6.1.0 are vulnerable to a stack-based buffer overflow, which may allow a local attacker to execute arbitrary code.

A crafted NTFS image can cause a heap-based buffer overflow in ntfs_mft_rec_alloc in NTFS-3G through 2021.8.22.

IBM i2 Analyst's Notebook 9.2.0, 9.2.1, and 9.2.2 is vulnerable to a stack-based buffer overflow, caused by improper bounds checking. A local attacker could overflow a buffer and gain lower level privileges. IBM X-Force ID: 214439.

A stack based buffer overflow was found in the virtio-net device of QEMU. This issue occurs when flushing TX in the virtio_net_flush_tx function if guest features VIRTIO_NET_F_HASH_REPORT, VIRTIO_F_VERSION_1 and VIRTIO_NET_F_MRG_RXBUF are enabled. This could allow a malicious user to overwrite local variables allocated on the stack. Specifically, the `out_sg` variable could be used to read a part of process memory and send it to the wire, causing an information leak.

The keycompare_mb function in sort.c in sort in GNU Coreutils through 8.23 on 64-bit platforms performs a size calculation without considering the number of bytes occupied by multibyte characters, which allows attackers to cause a denial of service (heap-based buffer overflow and application crash) or possibly have unspecified other impact via long UTF-8 strings.

An out-of-bounds write vulnerability was found in the virtio vhost-user GPU device (vhost-user-gpu) of QEMU in versions up to and including 6.0. The flaw occurs while processing the 'VIRTIO_GPU_CMD_GET_CAPSET' command from the guest. It could allow a privileged guest user to crash the QEMU process on the host, resulting in a denial of service condition, or potential code execution with the privileges of the QEMU process.

Bootloader contains a vulnerability in NVIDIA MB2 where potential heap overflow might cause corruption of the heap metadata, which might lead to arbitrary code execution, denial of service, and information disclosure during secure boot.

Stack based buffer overflow in le_ecred_conn_req(). Zephyr versions >= v2.5.0 Stack-based Buffer Overflow (CWE-121). For more information, see https://github.com/zephyrproject-rtos/zephyr/security/advisories/GHSA-8w87-6rfp-cfrm

NVIDIA Tegra kernel driver contains a vulnerability in NVIDIA NVDEC, where a user with high privileges might be able to read from or write to a memory location that is outside the intended boundary of the buffer, which may lead to denial of service, Information disclosure, loss of Integrity, or possible escalation of privileges.

This vulnerability allows local attackers to escalate privileges on affected installations of Parallels Desktop 16.1.3 (49160). An attacker must first obtain the ability to execute high-privileged code on the target guest system in order to exploit this vulnerability. The specific flaw exists within the virtio-gpu virtual device. The issue results from the lack of proper validation of user-supplied data, which can result in a memory corruption condition. An attacker can leverage this vulnerability to escalate privileges and execute arbitrary code in the context of the hypervisor. Was ZDI-CAN-13581.

An issue was discovered in net/rds/af_rds.c in the Linux kernel before 4.11. There is an out of bounds write and read in the function rds_recv_track_latency.

Trusty contains a vulnerability in all trusted applications (TAs) where the stack cookie was not randomized, which might result in stack-based buffer overflow, leading to denial of service, escalation of privileges, and information disclosure.

Out-of-bounds write in the Intel(R) Kernelflinger project may allow an authenticated user to potentially enable escalation of privilege via local access.

An issue was discovered in drivers/i2c/i2c-core-smbus.c in the Linux kernel before 4.14.15. There is an out of bounds write in the function i2c_smbus_xfer_emulated.

A flaw was found in the Linux kernel in versions before 5.9-rc6. When changing screen size, an out-of-bounds memory write can occur leading to memory corruption or a denial of service. Due to the nature of the flaw, privilege escalation cannot be fully ruled out.

IrfanView 4.53 allows a User Mode Write AV starting at WSQ!ReadWSQ+0x0000000000004359.

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

The netfilter subsystem in the Linux kernel before 4.9 mishandles IPv6 reassembly, which allows local users to cause a denial of service (integer overflow, out-of-bounds write, and GPF) or possibly have unspecified other impact via a crafted application that makes socket, connect, and writev system calls, related to net/ipv6/netfilter/nf_conntrack_reasm.c and net/ipv6/netfilter/nf_defrag_ipv6_hooks.c.

A memory corruption issue was addressed with improved state management. This issue is fixed in Security Update 2021-005 Catalina, macOS Big Sur 11.6. A local attacker may be able to elevate their privileges.

A local attacker may be able to elevate their privileges. This issue is fixed in macOS Big Sur 11.4, Security Update 2021-003 Catalina, Security Update 2021-004 Mojave. A memory corruption issue was addressed with improved validation.

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.

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. 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.

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.

IBM Security Verify Access 20.07 is vulnerable to a stack based buffer overflow, caused by improper bounds checking which could allow a local attacker to execute arbitrary code on the system with elevated privileges.

FS: Buffer Overflow when enabling Long File Names in FAT_FS and calling fs_stat. Zephyr versions >= v1.14.2, >= v2.3.0 contain Stack-based Buffer Overflow (CWE-121). For more information, see https://github.com/zephyrproject-rtos/zephyr/security/advisories/GHSA-7fhv-rgxr-x56h

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.

Out of Bounds Write and Read in AMD Graphics Driver for Windows 10 in Escape 0x6002d03 may lead to escalation of privilege or denial of service.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset. 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.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. 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 Buffer Overflow in AMD Graphics Driver for Windows 10 in Escape 0x15002a may lead to escalation of privilege or denial of service.

A heap overflow in LzmaUefiDecompressGetInfo function in EDK II.

A remote code execution vulnerability exists when the Windows Font Driver Host improperly handles memory.An attacker who successfully exploited the vulnerability would gain execution on a victim system.The security update addresses the vulnerability by correcting how the Windows Font Driver Host handles memory., aka 'Windows Font Driver Host Remote Code Execution Vulnerability'.