Bootloader contains a vulnerability in NVIDIA MB2 where a potential heap overflow might lead to denial of service or escalation of privileges.

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

An issue was discovered in net/rds/af_rds.c in the Linux kernel before 4.11. There is an out of bounds write and read in the function rds_recv_track_latency.

Certain NETGEAR devices are affected by a stack-based buffer overflow. This affects R6250 before 1.0.4.12, R6400v2 before 1.0.2.32, R7000P/R6900P before 1.0.0.56, R7900 before 1.0.1.18, R8300 before 1.0.2.100_1.0.82, R8500 before 1.0.2.100_1.0.82, and D8500 before 1.0.3.29.

objstack in GNU Aspell 0.60.8 has a heap-based buffer overflow in acommon::ObjStack::dup_top (called from acommon::StringMap::add and acommon::Config::lookup_list).

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.

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.

Stack based buffer overflow in le_ecred_conn_req(). Zephyr versions >= v2.5.0 Stack-based Buffer Overflow (CWE-121). For more information, see https://github.com/zephyrproject-rtos/zephyr/security/advisories/GHSA-8w87-6rfp-cfrm

A vulnerability has been found in NASM Netwide Assember 2.17rc0. Affected is the function parse_line of the file parser.c. The manipulation leads to stack-based buffer overflow. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used.

Win32k Elevation of Privilege Vulnerability

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.

Bootloader contains a vulnerability in NVIDIA MB2 where potential heap overflow might cause corruption of the heap metadata, which might lead to arbitrary code execution, denial of service, and information disclosure during secure boot.

A vulnerability was identified in neurobin shc up to 4.0.3. This issue affects the function make of the file src/shc.c. The manipulation leads to stack-based buffer overflow. The attack can only be performed from a local environment. The exploit is publicly available and might be used.

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

An issue was discovered on Samsung mobile devices with P(9.0) (Exynos chipsets) software. The Wi-Fi kernel drivers have a stack overflow. The Samsung IDs are SVE-2019-14965, SVE-2019-14966, SVE-2019-14968, SVE-2019-14969, SVE-2019-14970, SVE-2019-14980, SVE-2019-14981, SVE-2019-14982, SVE-2019-14983, SVE-2019-14984, SVE-2019-15122, SVE-2019-15123 (November 2019).

An issue was discovered in drivers/i2c/i2c-core-smbus.c in the Linux kernel before 4.14.15. There is an out of bounds write in the function i2c_smbus_xfer_emulated.

A vulnerability was found in Sricam IP CCTV Camera and classified as critical. This issue affects some unknown processing of the component Device Viewer. The manipulation leads to memory corruption. An attack has to be approached locally. The exploit has been disclosed to the public and may be used.

A vulnerability, which was classified as problematic, has been found in GNU Binutils 2.45. Affected by this issue is the function bfd_elf_set_group_contents of the file bfd/elf.c. The manipulation leads to out-of-bounds write. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used. The name of the patch is 41461010eb7c79fee7a9d5f6209accdaac66cc6b. It is recommended to apply a patch to fix this issue.

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.

An out-of-bound write can be triggered by a specially-crafted command supplied by a userspace application. in Snapdragon Auto, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in IPQ4019, IPQ8064, IPQ8074, MDM9150, MDM9206, MDM9607, MDM9640, MDM9650, MSM8996AU, QCA6174A, QCA6574AU, QCA8081, QCA9377, QCA9379, QCS605, SD 210/SD 212/SD 205, SD 425, SD 427, SD 430, SD 435, SD 450, SD 600, SD 625, SD 636, SD 675, SD 712 / SD 710 / SD 670, SD 730, SD 820, SD 820A, SD 835, SD 845 / SD 850, SD 855, SDM660, SDX20, SDX24

A remote code execution vulnerability exists when the Windows Font Driver Host improperly handles memory.An attacker who successfully exploited the vulnerability would gain execution on a victim system.The security update addresses the vulnerability by correcting how the Windows Font Driver Host handles memory., aka 'Windows Font Driver Host Remote Code Execution Vulnerability'.

This vulnerability allows local attackers to escalate privileges on affected installations of Parallels Desktop 16.1.0-48950. An attacker must first obtain the ability to execute low-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 the length of user-supplied data prior to copying it to a fixed-length stack-based buffer. An attacker can leverage this vulnerability to escalate privileges and execute arbitrary code in the context of the hypervisor. Was ZDI-CAN-12220.

In the Linux kernel, the following vulnerability has been resolved: net: amd-xgbe: Fix skb data length underflow There will be BUG_ON() triggered in include/linux/skbuff.h leading to intermittent kernel panic, when the skb length underflow is detected. Fix this by dropping the packet if such length underflows are seen because of inconsistencies in the hardware descriptors.

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.

A vulnerability was found in Sricam IP CCTV Camera. It has been classified as critical. Affected is an unknown function of the component Device Viewer. The manipulation leads to memory corruption. Local access is required to approach this attack.

In execTransact of Binder.java in Android 7.1.1, 7.1.2, 8.0, 8.1, and 9, there is a possible local execution of arbitrary code in a privileged process due to a memory overwrite. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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 1.0.0.110, 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 1.3.1.64, R7000P before 1.3.1.64, 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.

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 1.0.0.106, DGND2200Bv4 before 1.0.0.106, 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, R6400 before 1.0.1.42, R6700 before 1.0.1.46, R6700v3 before 1.0.2.52, R6900 before 1.0.1.46, R7000 before 1.0.9.28, 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 1.0.1.24, and WNR3500Lv2 before 1.2.0.54.

In the Linux kernel through 5.15.2, hw_atl_utils_fw_rpc_wait in drivers/net/ethernet/aquantia/atlantic/hw_atl/hw_atl_utils.c allows an attacker (who can introduce a crafted device) to trigger an out-of-bounds write via a crafted length value.

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).

The GTK version of ettercap uses a global settings file at /tmp/.ettercap_gtk and does not verify ownership of this file. When parsing this file for settings in gtkui_conf_read() (src/interfacesgtk/ec_gtk_conf.c), an unchecked sscanf() call allows a maliciously placed settings file to overflow a statically-sized buffer on the stack.

A local attacker may be able to elevate their privileges. This issue is fixed in macOS Big Sur 11.4, Security Update 2021-003 Catalina, Security Update 2021-004 Mojave. A memory corruption issue was addressed with improved validation.

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. 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.

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.

Improper validation of buffer size input to the EFS file can lead to memory corruption 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. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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 cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. 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 cause a heap buffer overflow in `tf.raw_ops.SparseSplit`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset. 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.

TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.

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.

Out of bound access can occur due to buffer copy without checking size of input received from WLAN firmware in Snapdragon Auto, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music in MDM9150, MDM9650, MSM8996AU, QCA6574AU, QCS405, QCS605, SD 210/SD 212/SD 205, SD 425, SD 427, SD 430, SD 435, SD 450, SD 625, SD 636, SD 665, SD 712 / SD 710 / SD 670, SD 730, SD 820A, SD 835, SD 845 / SD 850, SD 855, SDA660, SDM630, SDM660, SDX20, SDX24

Heap overflow in the BMC firmware for some Intel(R) Server Boards, Server Systems and Compute Modules before version 2.47 may allow an authenticated user to potentially enable escalation of privilege via local access.

TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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.