A local buffer overflow vulnerability exists in the latest version of Miniftpd in ftpproto.c through the tmp variable, where a crafted payload can be sent to the affected function.

Buffer Overflow vulnerability in Ffmpeg v.N113007-g8d24a28d06 allows a local attacker to execute arbitrary code via a floating point exception (FPE) error at libavfilter/vf_minterpolate.c:1078:60 in interpolate.

Some Honor products are affected by buffer overflow vulnerability, successful exploitation could cause code execution.

In the Linux kernel, the following vulnerability has been resolved: PM / devfreq: Fix buffer overflow in trans_stat_show Fix buffer overflow in trans_stat_show(). Convert simple snprintf to the more secure scnprintf with size of PAGE_SIZE. Add condition checking if we are exceeding PAGE_SIZE and exit early from loop. Also add at the end a warning that we exceeded PAGE_SIZE and that stats is disabled. Return -EFBIG in the case where we don't have enough space to write the full transition table. Also document in the ABI that this function can return -EFBIG error.

Buffer Overflow vulnerability in Ffmpeg v.N113007-g8d24a28d06 allows a local attacker to execute arbitrary code via the libavutil/imgutils.c:353:9 in image_copy_plane.

Buffer Copy without Checking Size of Input ('Classic Buffer Overflow'), Stack-based Buffer Overflow vulnerability in RTI Connext Professional (Core Libraries) allows Overflow Variables and Tags.This issue affects Connext Professional: from 7.4.0 before 7.5.0, from 7.0.0 before 7.3.0.7, from 6.1.0 before 6.1.2.23, from 6.0.0 before 6.0.1.42, from 5.3.0 before 5.3.*, from 4.5c before 5.2.*.

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.

A buffer overflow in Macrium Reflect 8.1.7544 and below allows attackers to escalate privileges or execute arbitrary code.

Memory corruption while copying a keyblob`s material when the key material`s size is not accurately checked.

Memory corruption while copying the sound model data from user to kernel buffer during sound model register.

Memory corruption in HLOS while running kernel address sanitizers (syzkaller) on tmecom with DEBUG_FS enabled.

Memory corruption when the bandpass filter order received from AHAL is not within the expected range.

Memory corruption while querying module parameters from Listen Sound model client in kernel from user space.

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.

NXP MCUXpresso SDK v2.7.0 was discovered to contain a buffer overflow in the function USB_HostProcessCallback().

In drivers/char/virtio_console.c in the Linux kernel before 5.13.4, data corruption or loss can be triggered by an untrusted device that supplies a buf->len value exceeding the buffer size. NOTE: the vendor indicates that the cited data corruption is not a vulnerability in any existing use case; the length validation was added solely for robustness in the face of anomalous host OS behavior

in OpenHarmony v4.1.2 and prior versions allow a local attacker cause the common permission is upgraded to root and sensitive information leak through buffer overflow.

Memory Corruption in Core Platform while printing the response buffer in log.

NXP MCUXpresso SDK v2.7.0 was discovered to contain a buffer overflow in the function USB_HostParseDeviceConfigurationDescriptor().

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.

Memory corruption in BT controller due to improper length check while processing vendor specific commands in Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking

Improper buffer initialization on the backend driver can lead to buffer overflow in Snapdragon Auto

Memory corruption in Core while processing control functions.

Memory corruption while processing finish_sign command to pass a rsp buffer.

Possible buffer overflow due to lack of validation for the length of NAI string read from EFS in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Mobile

Memory corruption in Audio while processing IIR config data from AFE calibration block.

Memory corruption in HLOS while running playready use-case.