Google Chrome before 11.0.696.57 on Linux does not properly interact with the X Window System, which allows remote attackers to cause a denial of service (application crash) via unspecified vectors.

Google Chrome before 10.0.648.127 on Linux does not properly handle parallel execution of calls to the print method, which might allow remote attackers to cause a denial of service (application crash) via crafted JavaScript code.

The Browser application in Android 4.0.3 allows remote attackers to cause a denial of service (application crash) via a crafted market: URI in the SRC attribute of an IFRAME element.

Google Chrome 1.0.154.48 and earlier allows remote attackers to cause a denial of service (CPU consumption and application hang) via JavaScript code with a long string value for the hash property (aka location.hash), a related issue to CVE-2008-5715.

An issue was discovered on Samsung mobile devices with M(6.0) software. SLocation can cause a system crash via a call to an API that is not implemented. The Samsung ID is SVE-2017-8285 (April 2017).

An issue was discovered on Samsung mobile devices with KK(4.4), L(5.0/5.1), and M(6.0) software. The InputMethod application can cause a system crash via a malformed serializable object in an Intent. The Samsung ID is SVE-2016-7123 (February 2017).

Improper input validation for some Intel Unison software may allow an unauthenticated user to potentially enable denial of service via network access.

An issue was discovered on Samsung mobile devices with N(7.0) software. The time service (aka Timaservice) allows a kernel panic. The Samsung ID is SVE-2017-8593 (May 2017).

In Messaging, there is a possible way to disable the messaging application due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

An issue was discovered on LG mobile devices with Android OS 9.0 and 10 software. The Wi-Fi subsystem has incorrect input validation, leading to a crash. The LG ID is LVE-SMP-200022 (October 2020).

An issue was discovered on LG mobile devices with Android OS 7.2, 8.0, 8.1, 9, and 10 software. An application crash can occur because of incorrect application-level input validation. The LG ID is LVE-SMP-200018 (July 2020).

An issue was discovered on LG mobile devices with Android OS 7.2, 8.0, 8.1, 9, and 10 software. A service crash may occur because of incorrect input validation. The LG ID is LVE-SMP-200013 (July 2020).

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.

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 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.

The XSL implementation in Google Chrome before 20.0.1132.43 allows remote attackers to cause a denial of service (incorrect read operation) via unspecified vectors.

The extension system in Google Chrome before 22.0.1229.79 does not properly handle modal dialogs, which allows remote attackers to cause a denial of service (application crash) via unspecified vectors.

Google Chrome before 20.0.1132.43 does not properly implement SVG filters, which allows remote attackers to cause a denial of service (out-of-bounds read) via unspecified vectors.

The base64DecodeInternal function in wtf/text/Base64.cpp in Blink, as used in Google Chrome before 34.0.1847.116, does not properly handle string data composed exclusively of whitespace characters, which allows remote attackers to cause a denial of service (out-of-bounds read) via a window.atob method call.

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-112923309

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-116267405

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-116351796

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 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-115747410

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-115372550

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-115375248

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-115747155

In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.

A denial of service vulnerability in the Android media framework (libhevc). Product: Android. Versions: 6.0, 6.0.1. Android ID: A-71567394.

TensorFlow is an open source platform for machine learning. If `ThreadUnsafeUnigramCandidateSampler` is given input `filterbank_channel_count` greater than the allowed max size, TensorFlow will crash. We have patched the issue in GitHub commit 39ec7eaf1428e90c37787e5b3fbd68ebd3c48860. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. An input `encoded` that is not a valid `CompositeTensorVariant` tensor will trigger a segfault in `tf.raw_ops.CompositeTensorVariantToComponents`. We have patched the issue in GitHub commits bf594d08d377dc6a3354d9fdb494b32d45f91971 and 660ce5a89eb6766834bdc303d2ab3902aef99d3d. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. If `tf.raw_ops.TensorListConcat` is given `element_shape=[]`, it results segmentation fault which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit fc33f3dc4c14051a83eec6535b608abe1d355fde. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. An input `token` that is not a UTF-8 bytestring will trigger a `CHECK` fail in `tf.raw_ops.PyFunc`. We have patched the issue in GitHub commit 9f03a9d3bafe902c1e6beb105b2f24172f238645. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. If `SparseFillEmptyRowsGrad` is given empty inputs, TensorFlow will crash. We have patched the issue in GitHub commit af4a6a3c8b95022c351edae94560acc61253a1b8. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. When running on GPU, `tf.image.generate_bounding_box_proposals` receives a `scores` input that must be of rank 4 but is not checked. We have patched the issue in GitHub commit cf35502463a88ca7185a99daa7031df60b3c1c98. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. Inputs `dense_features` or `example_state_data` not of rank 2 will trigger a `CHECK` fail in `SdcaOptimizer`. We have patched the issue in GitHub commit 80ff197d03db2a70c6a111f97dcdacad1b0babfa. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. An input `sparse_matrix` that is not a matrix with a shape with rank 0 will trigger a `CHECK` fail in `tf.raw_ops.SparseMatrixNNZ`. We have patched the issue in GitHub commit f856d02e5322821aad155dad9b3acab1e9f5d693. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

A denial of service vulnerability in the Android framework (package installer). Product: Android. Versions: 6.0, 6.0.1, 7.0, 7.1.1, 7.1.2, 8.0, 8.1. Android ID: A-62537081.

TensorFlow is an open source platform for machine learning. If `QuantizedInstanceNorm` is given `x_min` or `x_max` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `QuantizedBiasAdd` is given `min_input`, `max_input`, `min_bias`, `max_bias` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `RaggedBincount` is given an empty input tensor `splits`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 7a4591fd4f065f4fa903593bc39b2f79530a74b8. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `Requantize` is given `input_min`, `input_max`, `requested_output_min`, `requested_output_max` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. The implementation of `BlockLSTMGradV2` does not fully validate its inputs. This results in a a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 2a458fc4866505be27c62f81474ecb2b870498fa. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `QuantizedAdd` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. We have patched the issue in GitHub commit aa0b852a4588cea4d36b74feb05d93055540b450. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `SparseBincount` is given inputs for `indices`, `values`, and `dense_shape` that do not make a valid sparse tensor, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 40adbe4dd15b582b0210dfbf40c243a62f5119fa. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `QuantizedRelu` or `QuantizedRelu6` are given nonscalar inputs for `min_features` or `max_features`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.