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.

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.

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 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. 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.

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.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. 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. 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.

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.

TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of concatenation is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76). An attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of `int`. TFLite uses `int` to represent tensor dimensions, whereas TF uses `int64`. Hence, valid TF models can trigger an integer overflow when converted to TFLite format. 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 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.

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

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

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.

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.

Adobe Flash Player before 18.0.0.343 and 19.x through 21.x before 21.0.0.213 on Windows and OS X and before 11.2.202.616 on Linux allows attackers to execute arbitrary code or cause a denial of service (memory corruption) via unspecified vectors, a different vulnerability than CVE-2016-1012, CVE-2016-1020, CVE-2016-1021, CVE-2016-1022, CVE-2016-1023, CVE-2016-1025, CVE-2016-1026, CVE-2016-1027, CVE-2016-1028, CVE-2016-1029, CVE-2016-1032, and CVE-2016-1033.

Heap-based buffer overflow in the GPU process in Google Chrome before 10.0.648.205 allows remote attackers to execute arbitrary code via unknown vectors.

In libxaac there is a possible out of bounds write due to a missing bounds check. This could lead to remote code execution with no additional execution privileges needed. User interaction is needed for exploitation. Product: AndroidVersions: Android-10Android ID: A-114749542

An issue was discovered on Samsung mobile devices with N(7.x) and O(8.X) (Exynos chipsets) software. There is an arbitrary memory write in a Trustlet because a secure driver allows access to sensitive APIs. The Samsung ID is SVE-2018-12881 (November 2018).

Google Chrome before 11.0.696.71 does not properly handle blobs, which allows remote attackers to execute arbitrary code via unspecified vectors that trigger an out-of-bounds write.

In rw_t3t_act_handle_fmt_rsp of rw_t3t.cc, there is a possible out-of-bound write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is needed for exploitation.Product: AndroidVersions: Android-7.0 Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android-9Android ID: A-120497437

An issue was discovered on Samsung mobile devices with L(5.x), M(6.x), and N(7.x) software. There is a vnswap heap-based buffer overflow via the store function, with resultant privilege escalation. The Samsung ID is SVE-2017-10599 (January 2018).

Adobe Flash Player before 18.0.0.343 and 19.x through 21.x before 21.0.0.213 on Windows and OS X and before 11.2.202.616 on Linux allows attackers to execute arbitrary code or cause a denial of service (memory corruption) via unspecified vectors, a different vulnerability than CVE-2016-1012, CVE-2016-1020, CVE-2016-1021, CVE-2016-1022, CVE-2016-1023, CVE-2016-1024, CVE-2016-1025, CVE-2016-1026, CVE-2016-1027, CVE-2016-1029, CVE-2016-1032, and CVE-2016-1033.

An issue was discovered on Samsung mobile devices with M(6.0), N(7.x), and O(8.0) (Exynos chipsets) software. A kernel driver allows out-of-bounds Read/Write operations and possibly arbitrary code execution. The Samsung ID is SVE-2018-11358 (May 2018).

In the Easel driver, there is possible memory corruption due to race conditions. 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-112309571

Insufficient data validation in V8 in Google Chrome prior to 90.0.4430.93 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

An issue was discovered on Samsung mobile devices with O(8.x), P(9.0), Q(10.0), and R(11.0) (Exynos chipsets) software. The Mali GPU driver allows out-of-bounds access and a device reset. The Samsung ID is SVE-2020-19174 (January 2021).

Heap buffer overflow in Skia in Google Chrome prior to 121.0.6167.160 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Adobe Flash Player before 18.0.0.329 and 19.x and 20.x before 20.0.0.306 on Windows and OS X and before 11.2.202.569 on Linux, Adobe AIR before 20.0.0.260, Adobe AIR SDK before 20.0.0.260, and Adobe AIR SDK & Compiler before 20.0.0.260 allow attackers to execute arbitrary code or cause a denial of service (memory corruption) via unspecified vectors, a different vulnerability than CVE-2016-0964, CVE-2016-0965, CVE-2016-0966, CVE-2016-0967, CVE-2016-0968, CVE-2016-0969, CVE-2016-0970, CVE-2016-0972, CVE-2016-0976, CVE-2016-0977, CVE-2016-0978, CVE-2016-0980, and CVE-2016-0981.

In several native functions called by AdvertiseManager.java, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege in the Bluetooth server with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-9 Android-10 Android-11 Android-8.1Android ID: A-171400004

Adobe Flash Player before 18.0.0.329 and 19.x and 20.x before 20.0.0.306 on Windows and OS X and before 11.2.202.569 on Linux, Adobe AIR before 20.0.0.260, Adobe AIR SDK before 20.0.0.260, and Adobe AIR SDK & Compiler before 20.0.0.260 allow attackers to execute arbitrary code or cause a denial of service (memory corruption) via unspecified vectors, a different vulnerability than CVE-2016-0964, CVE-2016-0965, CVE-2016-0966, CVE-2016-0967, CVE-2016-0968, CVE-2016-0969, CVE-2016-0970, CVE-2016-0972, CVE-2016-0977, CVE-2016-0978, CVE-2016-0979, CVE-2016-0980, and CVE-2016-0981.

Unspecified vulnerability in Adobe Flash Player 21.0.0.242 and earlier, as used in the Adobe Flash libraries in Microsoft Internet Explorer 10 and 11 and Microsoft Edge, has unknown impact and attack vectors, a different vulnerability than other CVEs listed in MS16-083.

In WAVSource::read of WAVExtractor.cpp, there is a possible out of bounds write due to an integer overflow. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is needed for exploitation. Product: Android; Versions: Android-8.1, Android-9, Android-10, Android-11, Android-8.0; Android ID: A-170583712.

Insufficient validation of untrusted input in V8 in Google Chrome prior to 89.0.4389.128 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

An issue was discovered on Samsung mobile devices with software through 2015-11-12, affecting the Galaxy S6/S6 Edge, Galaxy S6 Edge+, and Galaxy Note5 with the Shannon333 chipset. There is a stack-based buffer overflow in the baseband process that is exploitable for remote code execution via a fake base station. The Samsung ID is SVE-2015-5123 (December 2015).

Heap buffer overflow in Blink in Google Chrome prior to 88.0.4324.96 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

A possible arbitrary memory overwrite vulnerabilities in quram library version prior to SMR Jan-2021 Release 1 allow arbitrary code execution.

Heap buffer overflow in V8 in Google Chrome prior to 90.0.4430.85 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page.

Execution of user supplied Javascript during object deserialization can update object length leading to an out of bounds write in V8 in Google Chrome prior to 71.0.3578.80 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page.

Incorrect, thread-unsafe use of SkImage in Canvas in Google Chrome prior to 71.0.3578.80 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Adobe Flash Player before 18.0.0.268 and 19.x and 20.x before 20.0.0.228 on Windows and OS X and before 11.2.202.554 on Linux, Adobe AIR before 20.0.0.204, Adobe AIR SDK before 20.0.0.204, and Adobe AIR SDK & Compiler before 20.0.0.204 allow attackers to execute arbitrary code or cause a denial of service (uninitialized pointer dereference and memory corruption) via crafted MPEG-4 data, a different vulnerability than CVE-2015-8045, CVE-2015-8047, CVE-2015-8060, CVE-2015-8408, CVE-2015-8416, CVE-2015-8417, CVE-2015-8418, CVE-2015-8419, CVE-2015-8443, CVE-2015-8444, CVE-2015-8451, CVE-2015-8455, CVE-2015-8652, CVE-2015-8654, CVE-2015-8656, CVE-2015-8657, and CVE-2015-8820.

Out of bounds memory access in V8 in Google Chrome prior to 90.0.4430.85 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Incorrect handing of paths leading to a use after free in Skia in Google Chrome prior to 71.0.3578.80 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in TabStrip in Google Chrome on Windows prior to 89.0.4389.114 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Insufficient data validation in V8 in Google Chrome prior to 90.0.4430.93 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.