TensorFlow is an end-to-end open source platform for machine learning. The TFLite code for allocating `TFLiteIntArray`s is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L24-L27). An attacker can craft a model such that the `size` multiplier is so large that the return value overflows the `int` datatype and becomes negative. In turn, this results in invalid value being given to `malloc`(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L47-L52). In this case, `ret->size` would dereference an invalid pointer. 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. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToSparse`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc#L30) has an incomplete validation of the splits values: it does not check that they are in increasing order. We have patched the issue in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece. 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 an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit f2a673bd34f0d64b8e40a551ac78989d16daad09. 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 all TFLite operations that use quantization can be made to use unitialized values. [For example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200). The issue stems from the fact that `quantization.params` is only valid if `quantization.type` is different that `kTfLiteNoQuantization`. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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. In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in `BoostedTreesCalculateBestGainsPerFeature` and similar attack can occur in `BoostedTreesCalculateBestFeatureSplitV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. We have patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7. 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.

Improper access control vulnerability in DesktopSystemUI prior to SMR Aug-2022 Release 1 allows attackers to enable and disable arbitrary components.

Improper validation of removing package name in Galaxy Themes prior to SMR May-2022 Release 1 allows attackers to uninstall arbitrary packages without permission. The patch adds proper validation logic for removing package name.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.EditDistance` has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service. In multiple places throughout the code, one may compute an index for a write operation. However, the existing validation only checks against the upper bound of the array. Hence, it is possible to write before the array by massaging the input to generate negative values for `loc`. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixSetDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit ff8894044dfae5568ecbf2ed514c1a37dc394f1b. 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. If a user does not provide a valid padding value to `tf.raw_ops.MatrixDiagPartOp`, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. We have patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988. 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. 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.

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.

An improper access control in ActivityManagerService prior to SMR APR-2021 Release 1 allows untrusted applications to access running processesdelete some local files.

SQL injection vulnerabilities in CMFA framework prior to SMR Oct-2021 Release 1 allow untrusted application to overwrite some CMFA framework information.

Improper memory access control in RKP in Samsung mobile devices prior to SMR Mar-2021 Release 1 allows an attacker, given a compromised kernel, to write certain part of RKP EL2 memory region.

An improper permission management in CertInstaller prior to SMR APR-2021 Release 1 allows untrusted applications to delete certain local files.

NVIDIA SHIELD TV, all versions prior to 8.2.2, contains a vulnerability in the NVHost function, which may lead to abnormal reboot due to a null pointer reference, causing data loss.

In all Qualcomm products with Android releases from CAF using the Linux kernel, user controlled variables "nr_cmds" and "nr_bos" number are passed across functions without any check. An integer overflow to buffer overflow (with a smaller buffer allocated) may occur when they are too large or negative.

Integer overflow in PDFium in Google Chrome prior to 74.0.3729.108 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.

In all Qualcomm products with Android releases from CAF using the Linux kernel, a race condition exists in an IOCTL handler potentially leading to an integer overflow and then an out-of-bounds write.

Integer overflow in PDFium in Google Chrome prior to 76.0.3809.87 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.

In AllocateInternalBuffers of g3aa_buffer_allocator.cc, 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.Product: AndroidVersions: Android kernelAndroid ID: A-222408847References: N/A

Integer overflow in ANGLE in Google Chrome on Windows prior to 74.0.3729.108 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Integer overflow in PDFium in Google Chrome prior to 76.0.3809.87 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.

In fdt_next_tag of fdt.c, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-12L Android-13Android ID: A-242096164

An integer overflow in FFmpeg in Google Chrome prior to 57.0.2987.98 for Mac, Windows, and Linux and 57.0.2987.108 for Android allowed a remote attacker to perform an out of bounds memory write via a crafted video file, related to ChunkDemuxer.

An integer overflow in Skia in Google Chrome prior to 62.0.3202.62 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page, aka an out-of-bounds write.

Integer overflow in PDFium in Google Chrome prior to 73.0.3683.75 allowed a remote attacker to potentially perform out of bounds memory access via a crafted PDF file.

A numeric overflow in Skia in Google Chrome prior to 58.0.3029.81 for Linux, Windows, and Mac, and 58.0.3029.83 for Android, allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page.

An integer overflow in FFmpeg in Google Chrome prior to 57.0.2987.98 for Mac, Windows, and Linux and 57.0.2987.108 for Android allowed a remote attacker to perform an out of bounds memory write via a crafted video file, related to ChunkDemuxer.

Adobe Flash Player versions 24.0.0.194 and earlier have an exploitable integer overflow vulnerability related to Flash Broker COM. Successful exploitation could lead to arbitrary code execution.

In ffu_flash_pack of ffu.c, there is a possible out of bounds read due to an integer overflow. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation.

An Integer Overflow exists in WebKit in Google Chrome before Blink M11 in the macOS WebCore::GraphicsContext::fillRect function.

Integer overflow in SQLite via WebSQL in Google Chrome prior to 74.0.3729.131 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Integer overflow in PDFium in Google Chrome prior to 74.0.3729.108 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.

In wma_ndp_end_response_event_handler(), the variable len_end_rsp is a uint32 which can be overflowed if the value of variable "event->num_ndp_end_rsp_per_ndi_list" is very large which can then lead to a heap overwrite of the heap object end_rsp in all Android releases from CAF (Android for MSM, Firefox OS for MSM, QRD Android) using the Linux Kernel.

Integer overflow in download manager in Google Chrome prior to 75.0.3770.80 allowed a remote attacker to potentially perform out of bounds memory access via a crafted HTML page.

An integer overflow leading to an incorrect capacity of a buffer in JavaScript in Google Chrome prior to 73.0.3683.75 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page.

In wma_peer_info_event_handler() in Android for MSM, Firefox OS for MSM, and QRD Android before 2017-10-03, the value of num_peers received from firmware is not properly validated so that an integer overflow vulnerability in the size of a buffer allocation may potentially lead to a buffer overflow.

An issue was discovered on Samsung mobile devices with M(6.x) and N(7.x) software. There is an Integer Overflow in process_M_SetTokenTUIPasswd during handling of a trusted application, leading to memory corruption. The Samsung IDs are SVE-2017-9008 and SVE-2017-9009 (October 2017).

In all Qualcomm products with Android releases from CAF using the Linux kernel, multiple values received from firmware are not properly validated in wma_get_ll_stats_ext_buf() and are used to allocate the sizes of buffers and may be vulnerable to integer overflow leading to buffer overflow.

Integer overflow in PDFium in Google Chrome prior to 73.0.3683.75 allowed a remote attacker to potentially perform out of bounds memory access via a crafted PDF file.

An integer overflow that leads to a use-after-free in Blink Storage in Google Chrome on Linux prior to 73.0.3683.75 allowed a remote attacker who had compromised the renderer process to execute arbitrary code via a crafted HTML page.

An integer overflow that leads to a use-after-free in WebMIDI in Google Chrome on Windows prior to 73.0.3683.75 allowed a remote attacker who had compromised the renderer process to execute arbitrary code via a crafted HTML page.

Integer overflow in Adobe Flash Player before 10.3.183.75 and 11.x before 11.7.700.169 on Windows and Mac OS X, before 10.3.183.75 and 11.x before 11.2.202.280 on Linux, before 11.1.111.50 on Android 2.x and 3.x, and before 11.1.115.54 on Android 4.x; Adobe AIR before 3.7.0.1530; and Adobe AIR SDK & Compiler before 3.7.0.1530 allows remote attackers to execute arbitrary code via unspecified vectors, as demonstrated by VUPEN during a Pwn2Own competition at CanSecWest 2013.

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `tf.math.segment_*` operations results in a `CHECK`-fail related abort (and denial of service) if a segment id in `segment_ids` is large. This is similar to CVE-2021-29584 (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using `AddDim`. However, if the number of elements in the tensor overflows an `int64_t` value, `AddDim` results in a `CHECK` failure which provokes a `std::abort`. Instead, code should use `AddDimWithStatus`. 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 TensorFlow allows tensor to have a large number of dimensions and each dimension can be as large as desired. However, the total number of elements in a tensor must fit within an `int64_t`. If an overflow occurs, `MultiplyWithoutOverflow` would return a negative result. In the majority of TensorFlow codebase this then results in a `CHECK`-failure. Newer constructs exist which return a `Status` instead of crashing the binary. This is similar to CVE-2021-29584. 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.

In prepare_io_entry and prepare_response of lwis_ioctl.c and lwis_periodic_io.c, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-205995773References: N/A