Possible out of bound memory access while playing a crafted clip in media player in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in SM8150

Microsoft PostScript Printer Driver Remote Code Execution Vulnerability

mlocate's %post script allows RUN_UPDATEDB_AS user to make arbitrary files world readable by abusing insecure file operations that run with root privileges.

Possible buffer overflow and over read possible due to missing bounds checks for fixed limits if we consider widevine HLOS client as non-trustable in Snapdragon Auto, Snapdragon Compute, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking in Kamorta, QCS404, Rennell, SC7180, SDX55, SM6150, SM7150, SM8250, SXR2130

Out of bound read/write in system firmware for Intel(R) NUC Kit may allow a privileged user to potentially enable escalation of privilege, denial of service and/or information disclosure via local access.

Out of bound read/write in system firmware for Intel(R) NUC Kit may allow a privileged user to potentially enable escalation of privilege, denial of service and/or information disclosure via local access.

Buffer over-read can occur in fast message handler due to improper input validation while processing a message from firmware in Snapdragon Auto, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music in APQ8053, APQ8096AU, MSM8996AU, MSM8998, QCN7605, QCS405, QCS605, SDA660, SDM636, SDM660, SDX20, SDX24

Lack of check of extscan change results received from firmware can lead to an out of buffer read in Snapdragon Auto, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music in MDM9150, MDM9206, MDM9607, MDM9640, MDM9650, MSM8996AU, QCA6174A, QCA6574AU, QCA9377, QCA9379, QCS605, SD 210/SD 212/SD 205, SD 425, SD 430, SD 600, SD 625, SD 636, SD 665, SD 675, SD 712 / SD 710 / SD 670, SD 730, SD 820A, SD 835, SD 845 / SD 850, SD 855, SDA660, SDM630, SDM660, SDX20, SDX24

Possibility of out of bound access in debug queue, if packet size field is corrupted in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8009, APQ8017, APQ8053, APQ8096AU, APQ8098, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8909W, MSM8917, MSM8920, MSM8937, MSM8940, MSM8953, MSM8996AU, MSM8998, Nicobar, QCN7605, QCS405, QCS605, QM215, SA6155P, SDA660, SDA845, SDM429, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM670, SDM710, SDM845, SDX20, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130

Possible OOB issue in EEPROM due to lack of check while accessing memory map array at the time of reading operation in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8009, APQ8053, MSM8909W, MSM8917, MSM8953, Nicobar, QCS405, QCS605, QM215, SA6155P, SDA845, SDM429, SDM439, SDM450, SDM632, SDM670, SDM710, SDM845, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130

Netwide Assembler (NASM) 2.13.02rc2 has a buffer over-read in the parse_line function in asm/parser.c via uncontrolled access to nasm_reg_flags.

In the Mediatek Preloader, there are out of bounds reads and writes due to an exposed interface that allows arbitrary peripheral memory mapping with insufficient blacklisting/whitelisting. This could lead to local elevation of privilege, given physical access to the device with no additional execution privileges needed. User interaction is needed for exploitation.

HUAWEI nova 4 versions earlier than 10.0.0.165(C01E34R2P4) and SydneyM-AL00 versions earlier than 10.0.0.165(C00E66R1P5) have an out-of-bounds read and write vulnerability. An attacker with specific permissions crafts malformed packet with specific parameter and sends the packet to the affected products. Due to insufficient validation of packet, which may be exploited to cause the information leakage or arbitrary code execution.

In valid_address of syscall.c, there is a possible out of bounds read due to an incorrect bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In V4L2SliceVideoDecodeAccelerator::Dequeue of v4l2_slice_video_decode_accelerator.cc, there is a possible out of bounds read of a function pointer due to an incorrect bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android-8.1 Android-9. Android ID: A-112181526.

While processing the system path, an out of bounds access can occur in Android releases from CAF using the linux kernel (Android for MSM, Firefox OS for MSM, QRD Android) before security patch level 2018-06-05.

While processing the USB StrSerialDescriptor array, an array index out of bounds can occur in Android releases from CAF using the linux kernel (Android for MSM, Firefox OS for MSM, QRD Android) before security patch level 2018-06-05.

An Out-Of-Bounds Read Vulnerability in Autodesk FBX SDK version 2020. and prior may lead to code execution or information disclosure through maliciously crafted FBX files. This vulnerability in conjunction with other vulnerabilities could lead to code execution in the context of the current process.

Memory corruption while processing FIPS encryption or decryption validation functionality IOCTL call.

An issue was discovered in the Linux kernel before 4.20. drivers/phy/mscc/phy-ocelot-serdes.c has an off-by-one error with a resultant ctrl->phys out-of-bounds read.

Memory corruption occurs while processing command received from HLOS due to improper length check in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables

In the Linux kernel 4.14.x, 4.15.x, 4.16.x, 4.17.x, and 4.18.x before 4.18.13, faulty computation of numeric bounds in the BPF verifier permits out-of-bounds memory accesses because adjust_scalar_min_max_vals in kernel/bpf/verifier.c mishandles 32-bit right shifts.

An OOB heap buffer r/w access issue was found in the NVM Express Controller emulation in QEMU. It could occur in nvme_cmb_ops routines in nvme device. A guest user/process could use this flaw to crash the QEMU process resulting in DoS or potentially run arbitrary code with privileges of the QEMU process.

An issue was discovered in xenvif_set_hash_mapping in drivers/net/xen-netback/hash.c in the Linux kernel through 4.18.1, as used in Xen through 4.11.x and other products. The Linux netback driver allows frontends to control mapping of requests to request queues. When processing a request to set or change this mapping, some input validation (e.g., for an integer overflow) was missing or flawed, leading to OOB access in hash handling. A malicious or buggy frontend may cause the (usually privileged) backend to make out of bounds memory accesses, potentially resulting in one or more of privilege escalation, Denial of Service (DoS), or information leaks.

Buffer Over-read in GitHub repository bfabiszewski/libmobi prior to 0.11. This vulnerability is capable of arbitrary code execution.

In __f2fs_setxattr in fs/f2fs/xattr.c in the Linux kernel through 5.15.11, there is an out-of-bounds memory access when an inode has an invalid last xattr entry.

The snd_msndmidi_input_read function in sound/isa/msnd/msnd_midi.c in the Linux kernel through 4.11.7 allows local users to cause a denial of service (over-boundary access) or possibly have unspecified other impact by changing the value of a message queue head pointer between two kernel reads of that value, aka a "double fetch" vulnerability.

The snd_msnd_interrupt function in sound/isa/msnd/msnd_pinnacle.c in the Linux kernel through 4.11.7 allows local users to cause a denial of service (over-boundary access) or possibly have unspecified other impact by changing the value of a message queue head pointer between two kernel reads of that value, aka a "double fetch" vulnerability.

TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in 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.

A possible out-of-bounds read and write (due to an improper length check of shared memory) was discovered in Arm NN Android-NN-Driver before 23.02.

The IPv6 fragmentation implementation in the Linux kernel through 4.11.1 does not consider that the nexthdr field may be associated with an invalid option, which allows local users to cause a denial of service (out-of-bounds read and BUG) or possibly have unspecified other impact via crafted socket and send system calls.

A local privilege escalation vulnerability was found on polkit's pkexec utility. The pkexec application is a setuid tool designed to allow unprivileged users to run commands as privileged users according predefined policies. The current version of pkexec doesn't handle the calling parameters count correctly and ends trying to execute environment variables as commands. An attacker can leverage this by crafting environment variables in such a way it'll induce pkexec to execute arbitrary code. When successfully executed the attack can cause a local privilege escalation given unprivileged users administrative rights on the target machine.

A crafted NTFS image can cause an out-of-bounds read in ntfs_runlists_merge_i in NTFS-3G < 2021.8.22.

The ip_cmsg_recv_checksum function in net/ipv4/ip_sockglue.c in the Linux kernel before 4.10.1 has incorrect expectations about skb data layout, which allows local users to cause a denial of service (buffer over-read) or possibly have unspecified other impact via crafted system calls, as demonstrated by use of the MSG_MORE flag in conjunction with loopback UDP transmission.

A crafted NTFS image can trigger an out-of-bounds read, caused by an invalid attribute in ntfs_attr_find_in_attrdef, in NTFS-3G < 2021.8.22.

Microsoft Windows SCSI Class System File Elevation of Privilege Vulnerability

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. 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.

TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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.

The eBPF ALU32 bounds tracking for bitwise ops (AND, OR and XOR) in the Linux kernel did not properly update 32-bit bounds, which could be turned into out of bounds reads and writes in the Linux kernel and therefore, arbitrary code execution. This issue was fixed via commit 049c4e13714e ("bpf: Fix alu32 const subreg bound tracking on bitwise operations") (v5.13-rc4) and backported to the stable kernels in v5.12.4, v5.11.21, and v5.10.37. The AND/OR issues were introduced by commit 3f50f132d840 ("bpf: Verifier, do explicit ALU32 bounds tracking") (5.7-rc1) and the XOR variant was introduced by 2921c90d4718 ("bpf:Fix a verifier failure with xor") ( 5.10-rc1).

In tmu_tz_control of tmu.c, there is a possible out of bounds read due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

Possible out of bound read or write in VR service due to lack of validation of DSP selection values in Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while processing a gpt update, an out of bounds memory access may potentially occur.

Possible out of bound read due to lack of domain input validation while processing APK close session request in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Wearables

TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB read on heap in the TFLite implementation of `Split_V`(https://github.com/tensorflow/tensorflow/blob/c59c37e7b2d563967da813fa50fe20b21f4da683/tensorflow/lite/kernels/split_v.cc#L99). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the `SizeOfDimension` function(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/kernel_util.h#L148-L150) will access data outside the bounds of the tensor shape array. 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.