An exploitable stack buffer overflow vulnerability vulnerability exists in the iocheckd service ‘I/O-Check’ functionality of WAGO PFC 200 Firmware version 03.02.02(14). An attacker can send a specially crafted packet to trigger the parsing of this cache file. The destination buffer sp+0x440 is overflowed with the call to sprintf() for any ip values that are greater than 1024-len(‘/etc/config-tools/config_interfaces interface=X1 state=enabled ip-address=‘) in length. A ip value of length 0x3da will cause the service to crash.

An exploitable stack buffer overflow vulnerability vulnerability exists in the iocheckd service ‘I/O-Check’ functionality of WAGO PFC 200 Firmware version 03.02.02(14). A specially crafted XML cache file written to a specific location on the device can cause a stack buffer overflow, resulting in code execution. An attacker can send a specially crafted packet to trigger the parsing of this cache file. The destination buffer sp+0x440 is overflowed with the call to sprintf() for any subnetmask values that are greater than 1024-len(‘/etc/config-tools/config_interfaces interface=X1 state=enabled subnet-mask=‘) in length. A subnetmask value of length 0x3d9 will cause the service to crash.

Certain NETGEAR devices are affected by a stack-based buffer overflow by an unauthenticated attacker. This affects D6220 before 1.0.0.44, D6400 before 1.0.0.78, D7000v2 before 1.0.0.51, D8500 before 1.0.3.42, DGN2200v4 before 1.0.0.110, DGND2200Bv4 before 1.0.0.109, EX3700 before 1.0.0.70, EX3800 before 1.0.0.70, EX6000 before 1.0.0.30, EX6100 before 1.0.2.24, EX6120 before 1.0.0.40, EX6130 before 1.0.0.22, EX6150v1 before 1.0.0.42, EX6200 before 1.0.3.88, EX7000 before 1.0.0.66, R6250 before 1.0.4.26, R6300v2 before 1.0.4.28, R6400 before 1.0.1.36, R6400v2 before 1.0.2.52, R6700 before 1.0.1.46, R6900 before 1.0.1.46, R7000 before 1.0.9.28, R6900P before 1.3.1.44, R7000P before 1.3.1.44, R7100LG before 1.0.0.46, R7300DST before 1.0.0.68, R7900 before 1.0.2.10, R8000 before 1.0.4.12, R7900P before 1.3.0.10, R8000P before 1.3.0.10, R8300 before 1.0.2.122, R8500 before 1.0.2.122, WN2500RPv2 before 1.0.1.54, WNDR3400v3 before 1.0.1.22, and WNR3500Lv2 before 1.2.0.54.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow. This occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values. 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.

K7 Antivirus Premium before 15.1.0.53 allows local users to write to arbitrary memory locations, and consequently gain privileges, via a specific set of IOCTL calls.

K7 Antivirus Premium before 15.1.0.53 allows local users to write to arbitrary memory locations, and consequently gain privileges, via a specific set of IOCTL calls.

A stack-based buffer overflow in Fortinet FortiWeb version 6.4.1 and 6.4.0, allows an authenticated attacker to execute unauthorized code or commands via crafted certificates loaded into the device.

K7 Antivirus Premium before 15.1.0.53 allows local users to write to arbitrary memory locations, and consequently gain privileges, via a specific set of IOCTL calls.

An issue was discovered on Samsung mobile devices with P(9.0) software. There is a heap overflow in the knox_kap driver. The Samsung ID is SVE-2019-14857 (November 2019).

An exploitable stack buffer overflow vulnerability vulnerability exists in the iocheckd service ‘I/O-Check’ functionality of WAGO PFC 200 Firmware version 03.02.02(14). An attacker can send a specially crafted packet to trigger the parsing of this cache file. The destination buffer sp+0x440 is overflowed with the call to sprintf() for any hostname values that are greater than 1024-len(‘/etc/config-tools/change_hostname hostname=‘) in length. A hostname value of length 0x3fd will cause the service to crash.

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 lwis_top_register_io of lwis_device_top.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-205995178References: 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

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

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

In the TitanM chip, 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 kernelAndroid ID: A-202006191References: N/A

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 copy_from_mbox of sss_ice_util.c, 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 kernelAndroid ID: A-202003354References: N/A

In kernel/bpf/hashtab.c in the Linux kernel through 5.13.8, there is an integer overflow and out-of-bounds write when many elements are placed in a single bucket. NOTE: exploitation might be impractical without the CAP_SYS_ADMIN capability.

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.

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: ALPS07453613; Issue ID: ALPS07453613.

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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Micron Crucial MX500 Series Solid State Drives M3CR046 is vulnerable to Buffer Overflow, which can be triggered by sending specially crafted ATA packets from the host to the drive controller.

vim is vulnerable to Heap-based Buffer Overflow

In preloader, 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: ALPS07734004 / ALPS07874358 (For MT6880, MT6890, MT6980, MT6990 only); Issue ID: ALPS07734004 / ALPS07874358 (For MT6880, MT6890, MT6980, MT6990 only).

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. We have patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

An out-of-bounds write flaw was found in the UAS (USB Attached SCSI) device emulation of QEMU in versions prior to 6.2.0-rc0. The device uses the guest supplied stream number unchecked, which can lead to out-of-bounds access to the UASDevice->data3 and UASDevice->status3 fields. A malicious guest user could use this flaw to crash QEMU or potentially achieve code execution with the privileges of the QEMU process on the host.

In encode of wlandata.cpp, there is a possible out of bounds write due to a heap buffer 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-263783137References: N/A

Multiple stack-based buffer overflows in the command line interpreter of FortiWeb before 6.4.2 may allow an authenticated attacker to achieve arbitrary code execution via specially crafted commands.

A Buffer Overflow in Thrift command handlers in IDEMIA Morpho Wave Compact and VisionPass devices before 2.6.2 allows physically proximate authenticated attackers to achieve code execution, denial of services, and information disclosure via serial ports.

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.

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.

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.

OpenThread wpantund through 2021-07-02 has a stack-based Buffer Overflow because of an inconsistency in the integer data type for metric_len.

Trusty contains a vulnerability in the HDCP service TA where bounds checking in command 10 is missing. The length of an I/O buffer parameter is not checked, which might lead to memory corruption.

Bootloader contains a vulnerability in NVIDIA MB2 where a potential heap overflow could cause memory corruption, which might lead to denial of service or code execution.

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.

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 Toolgate component. The issue results from the lack of proper validation of user-supplied data, which can result in a write past the end of an allocated buffer. An attacker can leverage this vulnerability to escalate privileges and execute arbitrary code in the context of the hypervisor. Was ZDI-CAN-13601.

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

When sending malicous data to kernel by ioctl cmd FBIOPUT_VSCREENINFO,kernel will write memory out of bounds.

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.

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. 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.

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.

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.

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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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 write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. 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.