Out-of-bounds Read in GitHub repository radareorg/radare2 prior to 5.7.0. The bug causes the program reads data past the end of the intented buffer. Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash.

Out of bound memory access can happen while parsing ADSP message due to lack of check of size of payload received from userspace in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8096AU, IPQ4019, IPQ6018, IPQ8064, IPQ8074, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, QCN7605, QCS605, SC8180X, SDM710, SDX24, SDX55, SM8150, SM8250, SXR2130

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where an unprivileged regular user can cause an out-of-bounds read, which may lead to denial of service and information disclosure.

Out of bound read can happen in Widevine TA while copying data to buffer from user data due to lack of check of buffer length received in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking

Possible out of bound read in DRM due to improper buffer length check. in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking

VMware Workstation (15.x) and Horizon Client for Windows (5.x before 5.4.4) contain an out-of-bounds read vulnerability in Cortado ThinPrint component (EMF Parser). A malicious actor with normal access to a virtual machine may be able to exploit these issues to create a partial denial-of-service condition or to leak memory from TPView process running on the system where Workstation or Horizon Client for Windows is installed.

VMware Workstation (15.x) and Horizon Client for Windows (5.x before 5.4.4) contain an out-of-bounds read vulnerability in Cortado ThinPrint component (JPEG2000 parser). A malicious actor with normal access to a virtual machine may be able to exploit these issues to create a partial denial-of-service condition or to leak memory from TPView process running on the system where Workstation or Horizon Client for Windows is installed.

Improper boundary check in sflvd_rdbuf_bits of libsflvextractor prior to SMR Apr-2022 Release 1 allows attackers to read out of bounds memory.

Improper use of SMS buffer pointer in Shannon baseband prior to SMR Mar-2022 Release 1 allows OOB read.

An improper boundary check in audio hal service prior to SMR Feb-2022 Release 1 allows attackers to read invalid memory and it leads to application crash.

A security out-of-bounds read information disclosure vulnerability in Trend Micro Worry-Free Business Security Server could allow a local attacker to send garbage data to a specific named pipe and crash the server. Please note: an attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability.

Buffer over read could occur due to incorrect check of buffer size while flashing emmc devices in Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking

NVIDIA TrustZone Software contains a vulnerability in the Keymaster implementation where the software reads data past the end, or before the beginning, of the intended buffer; and may lead to denial of service or information disclosure. This issue is rated as high.

A flaw was found in dpdk in versions before 18.11.10 and before 19.11.5. A complete lack of validation of attacker-controlled parameters can lead to a buffer over read. The results of the over read are then written back to the guest virtual machine memory. This vulnerability can be used by an attacker in a virtual machine to read significant amounts of host memory. The highest threat from this vulnerability is to data confidentiality and system availability.

Buffer Over-read at parse_rawml.c:1416 in GitHub repository bfabiszewski/libmobi prior to 0.11. The bug causes the program reads data past the end of the intented buffer. Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash.

Out-of-bounds memory access can occur while calculating alignment requirements for a negative width from external components in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music

u'Buffer over read in boot due to size check ignored before copying GUID attribute from request to response' in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8096AU, APQ8098, MDM8207, MDM9150, MDM9205, MDM9206, MDM9207, MDM9250, MDM9607, MDM9628, MDM9650, MSM8108, MSM8208, MSM8209, MSM8608, MSM8905, MSM8909, MSM8998, QCM4290, QCS405, QCS410, QCS4290, QCS603, QCS605, QCS610, QSM8250, SA415M, SA515M, SA6145P, SA6150P, SA6155, SA6155P, SA8150P, SA8155, SA8155P, SA8195P, SC7180, SC8180X, SC8180X+SDX55, SC8180XP, SDA640, SDA670, SDA845, SDA855, SDM1000, SDM640, SDM670, SDM710, SDM712, SDM830, SDM845, SDM850, SDX24, SDX50M, SDX55, SDX55M, SM4125, SM4250, SM4250P, SM6115, SM6115P, SM6125, SM6150, SM6150P, SM6250, SM6250P, SM6350, SM7125, SM7150, SM7150P, SM7225, SM7250, SM7250P, SM8150, SM8150P, SM8250, SXR1120, SXR1130, SXR2130, SXR2130P, WCD9330

do_core_note in readelf.c in libmagic.a in file 5.35 has a stack-based buffer over-read, related to file_printable, a different vulnerability than CVE-2018-10360.

do_core_note in readelf.c in libmagic.a in file 5.35 has an out-of-bounds read because memcpy is misused.

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `FusedBatchNorm` kernels is vulnerable to a heap OOB access. 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.

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `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.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for the `QuantizeAndDequantizeV*` operations can trigger a read outside of bounds of heap allocated array. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. 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 (OOB) memory read flaw was found in the Qualcomm IPC router protocol in the Linux kernel. A missing sanity check allows a local attacker to gain access to out-of-bounds memory, leading to a system crash or a leak of internal kernel information. The highest threat from this vulnerability is to system availability.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can read data outside of bounds of heap allocated buffer in `tf.raw_ops.QuantizeAndDequantizeV3`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/11ff7f80667e6490d7b5174aa6bf5e01886e770f/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L237) does not validate the value of user supplied `axis` attribute before using it to index in the array backing the `input` argument. 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. Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131) accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same 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. An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads. 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 the Linux kernel through 5.11.3. drivers/scsi/scsi_transport_iscsi.c is adversely affected by the ability of an unprivileged user to craft Netlink messages.

Lack of boundary checking of a buffer in libSPenBase library of Samsung Notes prior to Samsung Note version 4.3.02.61 allows OOB read.

A component of the HarmonyOS has a Out-of-bounds Read vulnerability. Local attackers may exploit this vulnerability to cause kernel out-of-bounds read.

There is an out-of-bound read vulnerability in Taurus-AL00A 10.0.0.1(C00E1R1P1). A module does not verify the some input. Attackers can exploit this vulnerability by sending malicious input through specific app. This could cause out-of-bound, compromising normal service.

Possible out of bounds read due to incorrect validation of incoming buffer length in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile

Possible buffer over read due to lack of data length check in QVR Service configuration in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Wearables