In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."

Improper access control and path traversal vulnerability in LauncherProvider prior to SMR Aug-2022 Release 1 allow local attacker to access files of One UI.

Improper input validation in baseband prior to SMR Aug-2022 Release 1 allows attackers to cause integer overflow to heap overflow.

Improper validation vulnerability in CACertificateInfo prior to SMR Jul-2022 Release 1 allows attackers to launch certain activities.

In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly checked. Since each of the above methods can return an error status, the `status` value must be checked before continuing. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

The page serializer in Google Chrome before 47.0.2526.73 mishandles Mark of the Web (MOTW) comments for URLs containing a "--" sequence, which might allow remote attackers to inject HTML via a crafted URL, as demonstrated by an initial http://example.com?-- substring.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In all Android releases from CAF using the Linux kernel, a KGSL ioctl was not validating all of its parameters.

In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

In wpa_supplicant, there is a possible man in the middle vulnerability due to improper input validation of the basicConstraints field of intermediary certificates. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation. Product: AndroidVersions: Android-10Android ID: A-111893041

In onCreate of MediaProjectionPermissionActivity.java , there is a possible way to grant a malicious app a token enabling unauthorized screen recording capabilities 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.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. In this case, this results in a segmentation fault The issue is patched in commit da8558533d925694483d2c136a9220d6d49d843c, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In ih264d_decode_slice_thread of ih264d_thread_parse_decode.c, there is a possible out of bounds read due to improper input validation. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is needed for exploitation.Product: AndroidVersions: Android-10Android ID: A-144687080

Improper input validation in Contacts Storage prior to SMR Jul-2022 Release 1 allows attacker to access arbitrary file.

An issue was discovered on Samsung mobile devices with Q(10.0) (with TEEGRIS on Exynos chipsets) software. The Widevine Trustlet allows arbitrary code execution because of memory disclosure, The Samsung IDs are SVE-2020-17117, SVE-2020-17118, SVE-2020-17119, and SVE-2020-17161 (June 2020).

In onCreate of NotificationAccessConfirmationActivity.java, there is a possible way for an app in the work profile to enable notification listener services due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is needed for exploitation.

In smp_proc_sec_req of smp_act.cc, there is a possible out of bounds read due to improper input validation. This could lead to remote (proximal/adjacent) information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.

An issue was discovered on Samsung mobile devices with O(8.x) (with TEEGRIS) software. The Gatekeeper Trustlet allows a brute-force attack on user credentials. The Samsung ID is SVE-2020-16908 (June 2020).

In Bluetooth, there is possible controlled termination due to a missing bounds check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Product: AndroidVersions: Android-10Android ID: A-115745406

In mtk-aie, there is a possible use after free due to a logic error. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07262518; Issue ID: ALPS07262518.

Insufficient validation of untrusted input in DevTools in Google Chrome on Chrome OS prior to 105.0.5195.125 allowed an attacker who convinced a user to install a malicious extension to bypass navigation restrictions via a crafted HTML page. (Chromium security severity: High)

An issue was discovered on Samsung mobile devices with P(9.0) and Q(10.0) (with TEEGRIS) software. Attackers can determine user credentials via a brute-force attack against the Gatekeeper trustlet. The Samsung ID is SVE-2020-16908 (May 2020).

In mtk-aie, there is a possible use after free due to a logic error. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07262617; Issue ID: ALPS07262617.

Adobe Flash Player before 18.0.0.241 and 19.x before 19.0.0.185 on Windows and OS X and before 11.2.202.521 on Linux, Adobe AIR before 19.0.0.190, Adobe AIR SDK before 19.0.0.190, and Adobe AIR SDK & Compiler before 19.0.0.190 allow attackers to cause a denial of service (vector-length corruption) or possibly have unspecified other impact via unknown vectors.

A parsing issue with binary data in protobuf-java core and lite versions prior to 3.21.7, 3.20.3, 3.19.6 and 3.16.3 can lead to a denial of service attack. Inputs containing multiple instances of non-repeated embedded messages with repeated or unknown fields causes objects to be converted back-n-forth between mutable and immutable forms, resulting in potentially long garbage collection pauses. We recommend updating to the versions mentioned above.

Implicit Intent hijacking vulnerability in AppLinker prior to SMR Jul-2022 Release 1 allow allows attackers to launch certain activities with privilege of AppLinker.

Improper input validation check logic vulnerability in SECRIL prior to SMR Jun-2022 Release 1 allows attackers to trigger crash.

Improper input validation check logic vulnerability in libsmkvextractor prior to SMR Jun-2022 Release 1 allows attackers to trigger crash.

Improper validation vulnerability in KfaOptions prior to SMR Jun-2022 Release 1 allows attackers to launch certain activities.

Implicit Intent hijacking vulnerability in Finder prior to SMR Jul-2022 Release 1 allow allows attackers to launch certain activities with privilege of Finder.

Improper validation vulnerability in LSOItemData prior to SMR Jun-2022 Release 1 allows attackers to launch certain activities.

Improper validation vulnerability in FeedsInfo prior to SMR Jun-2022 Release 1 allows attackers to launch certain activities.

Improper input validation check logic vulnerability in libsmkvextractor prior to SMR Jun-2022 Release 1 allows attackers to trigger crash.

Improper validation vulnerability in RemoteViews prior to SMR Jun-2022 Release 1 allows attackers to launch certain activities.

The CyanogenMod/ClockWorkMod/Koush Superuser package 1.0.2.1 for Android allows attackers to gain privileges via shell metacharacters in the -c option to /system/xbin/su.

Improper input validation check logic vulnerability in libsmkvextractor prior to SMR Jun-2022 Release 1 allows attackers to trigger crash.

Insufficient data validation in Mojo in Google Chrome prior to 105.0.5195.102 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page.

Unprotected component vulnerability in DeviceSearchTrampoline in SecSettingsIntelligence prior to SMR Jun-2022 Release 1 allows local attackers to launch activities of SecSettingsIntelligence.

In newServiceInfoLocked of AutofillManagerServiceImpl.java, there is a possible way to hide an enabled Autofill service app in the Autofill service settings due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is needed for exploitation.

NVIDIA libnvomx contains a possible out of bounds write due to a improper input validation which could lead to local escalation of privilege. This issue is rated as high. Product: Android. Version: N/A. Android: A-66969318. Reference: N-CVE-2017-6281.

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.SparseTensorDenseAdd` does not fully validate the input arguments. In this case, a reference gets bound to a `nullptr` during kernel execution. This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.ragged.constant` does not fully validate the input arguments. This results in a denial of service by consuming all available memory. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.TensorSummaryV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.Conv3DBackpropFilterV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code does not validate that the `filter_sizes` argument is a vector. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.