In the Linux kernel, the following vulnerability has been resolved: iio: adc: tsc2046: fix memory corruption by preventing array overflow On one side we have indio_dev->num_channels includes all physical channels + timestamp channel. On other side we have an array allocated only for physical channels. So, fix memory corruption by ARRAY_SIZE() instead of num_channels variable. Note the first case is a cleanup rather than a fix as the software timestamp channel bit in active_scanmask is never set by the IIO core.
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
Possible out of bounds write due to improper validation of number of GPIOs configured in an internal parameters array in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile
In multiple functions of btm_ble_gap.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.
Out-of-bounds write in libsapeextractor.so prior to SMR Nov-2024 Release 1 allows local attackers to cause memory corruption.
Out-of-bound write in libcodec2secmp4vdec.so prior to SMR Aug-2024 Release 1 allows local attackers to execute arbitrary code.
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.
Out-of-bound write in libsmat.so prior to SMR Aug-2024 Release 1 allows local attackers to execute arbitrary code.
Possible buffer overflow due to improper size calculation of payload received in VR service in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Wearables
In vring_size of external/headers/include/virtio/virtio_ring.h, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.
Out-of-bounds write in appending paragraph in Samsung Notes prior to version 4.4.21.62 allows local attackers to potentially execute arbitrary code with Samsung Notes privilege.
Possible stack overflow due to improper length check of TLV while copying the TLV to a local stack variable in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking
Heap-based out-of-bounds write in Samsung Notes prior to version 4.4.21.62 allows local attackers to execute arbitrary code.
Out-of-bounds write in applying connected information in Samsung Notes prior to version 4.4.21.62 allows local attackers to potentially execute arbitrary code with Samsung Notes privilege.
In prepare_response_locked of lwis_transaction.c, there is a possible out of bounds write due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.
Memory corruption while passing untrusted/corrupted pointers from DSP to EVA.
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. 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.
Memory corruption when BTFM client sends new messages over Slimbus to ADSP.
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.
Memory corruption when user provides data for FM HCI command control operations.
Memory corruption during the handshake between the Primary Virtual Machine and Trusted Virtual Machine.
IBM Security Verify Access 20.07 is vulnerable to a stack based buffer overflow, caused by improper bounds checking which could allow a local attacker to execute arbitrary code on the system with elevated privileges.
In BCMFASTPATH of dhd_msgbuf.c, there is a possible out of bounds write 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.
IBM Spectrum Protect Client 8.1.0.0-8 through 1.11.0 is vulnerable to a stack-based buffer overflow, caused by improper bounds checking when processing the current locale settings. A local attacker could overflow a buffer and execute arbitrary code on the system with elevated privileges or cause the application to crash. IBM X-Force ID: 199479
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.
In v4l2_smfc_qbuf of smfc-v4l2-ioctls.c, there is a possible out of bounds write 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.
TensorFlow is an end-to-end open source platform for machine learning. An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. 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.
Memory corruption when Alternative Frequency offset value is set to 255.
In createWithSurfaceParent of Client.cpp, there is a possible out of bounds write due to type confusion. This could lead to local escalation of privilege in the graphics server with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10Android ID: A-150226994
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.
An issue was discovered in Samsung Mobile Processor and Wearable Processor Exynos 850, Exynos 1080, Exynos 2100, Exynos 1280, Exynos 1380, Exynos 1330, Exynos W920, Exynos W930. The mobile processor lacks proper length checking, which can result in an OOB (Out-of-Bounds) Write vulnerability.
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.
In increment_annotation_count of stats_event.c, there is a possible out of bounds write 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.
An issue was discovered in Samsung Mobile Processor Exynos 2200, Exynos 1480, Exynos 2400. It lacks proper buffer length checking, which can result in an Out-of-Bounds Write.
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-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. 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. 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. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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.
A vulnerability has been identified in JT2Go (All versions < V13.1.0.1), Teamcenter Visualization (All versions < V13.1.0.1). Affected applications lack proper validation of user-supplied data when parsing of TGA files. This could result in an out of bounds write past the end of an allocated structure. An attacker could leverage this vulnerability to execute code in the context of the current process. (ZDI-CAN-12178)
Out-of-bounds write for some Intel(R) QuickAssist Technology software before version 2.2.0 may allow an authenticated user to potentially enable escalation of privilege via local access.
In availableToWriteBytes of MessageQueueBase.h, there is a possible out of bounds write 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.
A vulnerability was found in appneta tcpreplay up to 4.4.4. It has been classified as problematic. This affects the function get_layer4_v6 of the file /tcpreplay/src/common/get.c. The manipulation leads to heap-based buffer overflow. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used. The identifier VDB-258333 was assigned to this vulnerability. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.
An issue was discovered in the Linux kernel through 5.11.3. Certain iSCSI data structures do not have appropriate length constraints or checks, and can exceed the PAGE_SIZE value. An unprivileged user can send a Netlink message that is associated with iSCSI, and has a length up to the maximum length of a Netlink message.
Windows DWM Core Library Elevation of Privilege Vulnerability
A malformed SMI (System Management Interface) command may allow an attacker to establish a corrupted SMI Trigger Info data structure, potentially leading to out-of-bounds memory reads and writes when triggering an SMI resulting in a potential loss of resources.
In String16 of String16.cpp, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege in an unprivileged process with no additional execution privileges needed. User interaction is not needed for exploitation.
Secure Boot Security Feature Bypass Vulnerability
The iowarrior_write function in drivers/usb/misc/iowarrior.c in the Linux kernel before 2.6.37 does not properly allocate memory, which might allow local users to trigger a heap-based buffer overflow, and consequently cause a denial of service or gain privileges, via a long report.
Stack-based buffer overflow in the RtlQueryRegistryValues function in win32k.sys in Microsoft Windows XP SP2 and SP3, Windows Server 2003 SP2, Windows Vista SP1 and SP2, Windows Server 2008 Gold, SP2, and R2, and Windows 7 allows local users to gain privileges, and bypass the User Account Control (UAC) feature, via a crafted REG_BINARY value for a SystemDefaultEUDCFont registry key, aka "Driver Improper Interaction with Windows Kernel Vulnerability."
An out-of-bounds write information disclosure vulnerability in Trend Micro Apex One (on-prem and SaaS), OfficeScan XG SP1, and Worry-Free Business Security (10.0 SP1 and Services) could allow a local attacker to escalate privileges on affected installations. Please note: an attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability.