An Out-of-Bounds Read Privilege Escalation vulnerability in Trend Micro Security 2018 (Consumer) products could allow a local attacker to escalate privileges on vulnerable installations. An attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit the vulnerability.

Out-of-bounds Read in mrb_obj_is_kind_of in in GitHub repository mruby/mruby prior to 3.2. # Impact: Possible arbitrary code execution if being exploited.

Out-of-Bounds access in TZ due to invalid index calculated to check against DDR in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in IPQ8074, MDM9206, MDM9607, MDM9650, MDM9655, MSM8996AU, QCA8081, Qualcomm 215, SD 410/12, SD 425, SD 427, SD 430, SD 435, SD 439 / SD 429, SD 450, SD 625, SD 632, SD 650/52, SD 820, SD 820A, SDM439, Snapdragon_High_Med_2016

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

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while processing diag event after associating to a network out of bounds read occurs if ssid of the network joined is greater than max limit.

In all android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, Buffer overread may occur due to non-null terminated strings while processing vsprintf in camera jpeg driver.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while processing start bss request from upper layer, out of bounds read occurs if ssid length is greater than maximum.

A vulnerability has been identified in SINUMERIK 808D V4.7 (All versions), SINUMERIK 808D V4.8 (All versions), SINUMERIK 828D V4.7 (All versions < V4.7 SP6 HF1), SINUMERIK 840D sl V4.7 (All versions < V4.7 SP6 HF5), SINUMERIK 840D sl V4.8 (All versions < V4.8 SP3). A local attacker could use ioctl calls to do out of bounds reads, arbitrary writes, or execute code in kernel mode. The security vulnerability could be exploited by an attacker with local access to the affected systems. Successful exploitation requires user privileges but no user interaction. The vulnerability could allow an attacker to compromise confidentiality, integrity and availability of the system. At the time of advisory publication no public exploitation of this security vulnerability was known.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, a buffer over-read can occur In the WMA NDP event handler functions due to lack of validation of input value event_info which is received from FW.

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 intr function in sound/oss/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 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.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, improper input validation for vdev_id in wma_unified_bcntx_status_event_handler() which is received from firmware leads to potential out of bounds memory read.

While reading file class type from ELF header, a buffer overread may happen if the ELF file size is less than the size of ELF64 header size in Small Cell SoC, Snapdragon Automobile, Snapdragon Mobile, Snapdragon Wear in version FSM9055, MDM9206, MDM9607, MDM9650, MSM8909W, MSM8996AU, SD 210/SD 212/SD 205, SD 425, SD 430, SD 450, SD 615/16/SD 415, SD 625, SD 650/52, SD 820, SD 820A, SD 835, SD 845, SDA660, SDX20.

Insufficient memory allocation in boot due to incorrect size being passed could result in out of bounds access in Small Cell SoC, Snapdragon Automobile, Snapdragon Mobile and Snapdragon Wear in version FSM9055, MDM9206, MDM9607, MDM9640, MDM9650, MSM8909W, MSM8996AU, SD 210/SD 212/SD 205, SD 425, SD 430, SD 450, SD 615/16/SD 415, SD 617, SD 625, SD 650/52, SD 810, SD 820, SD 820A, SD 835, SDA660 and SDX20

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 a StrHwPlatform with length smaller than EFICHIPINFO_MAX_ID_LENGTH, an array out of bounds access may occur.

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.

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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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 out-of-bounds read was addressed with improved bounds checking. This issue is fixed in macOS Big Sur 11.2, Security Update 2021-001 Catalina, Security Update 2021-001 Mojave, watchOS 7.3, tvOS 14.4, iOS 14.4 and iPadOS 14.4. A local attacker may be able to elevate their privileges.

Buffer over-read vulnerabilities in an older version of ASN.1 parser in Snapdragon Mobile in versions SD 600.

Out-of-bounds read in subsystem for Intel(R) AMT versions before 11.8.80, 11.12.80, 11.22.80, 12.0.70 and 14.0.45 may allow a privileged user to potentially enable escalation of privilege via local access.

In android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, possible buffer overflow or information leak in the functions "sme_set_ft_ies" and "csr_roam_issue_ft_preauth_req" due to incorrect initialization of WEXT callbacks and lack of the checks for buffer size.