In WIFI Firmware, there is a possible system crash due to a missing count check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06468894; Issue ID: ALPS06468894.

In bindArtworkAndColors of MediaControlPanel.java, there is a possible way to crash the phone 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.Product: AndroidVersions: Android-13Android ID: A-239368697

In LteRrcNrProAsnDecode of LteRrcNr_Codec.c, there is a possible out of bounds read 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 kernelAndroid ID: A-180956894References: N/A

In hostapd, there is a possible insecure configuration due to an insecure default value. This could lead to remote denial of service of the wifi hotspot with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-197874458

Product: AndroidVersions: Android kernelAndroid ID: A-210936609References: N/A

A parsing vulnerability for the MessageSet type in the ProtocolBuffers versions prior to and including 3.16.1, 3.17.3, 3.18.2, 3.19.4, 3.20.1 and 3.21.5 for protobuf-cpp, and versions prior to and including 3.16.1, 3.17.3, 3.18.2, 3.19.4, 3.20.1 and 4.21.5 for protobuf-python can lead to out of memory failures. A specially crafted message with multiple key-value per elements creates parsing issues, and can lead to a Denial of Service against services receiving unsanitized input. We recommend upgrading to versions 3.18.3, 3.19.5, 3.20.2, 3.21.6 for protobuf-cpp and 3.18.3, 3.19.5, 3.20.2, 4.21.6 for protobuf-python. Versions for 3.16 and 3.17 are no longer updated.

Product: AndroidVersions: Android kernelAndroid ID: A-210594998References: N/A

Unbounded memory allocation in Google Guava 11.0 through 24.x before 24.1.1 allows remote attackers to conduct denial of service attacks against servers that depend on this library and deserialize attacker-provided data, because the AtomicDoubleArray class (when serialized with Java serialization) and the CompoundOrdering class (when serialized with GWT serialization) perform eager allocation without appropriate checks on what a client has sent and whether the data size is reasonable.

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).

An issue was discovered on Samsung mobile devices with KK(4.4), L(5.0/5.1), and M(6.0) software. android.intent.action.SIOP_LEVEL_CHANGED allows a serializable intent reboot. The Samsung ID is SVE-2017-8363 (May 2017).

An issue was discovered on Samsung mobile devices with KK(4.4), L(5.0/5.1), M(6.0), and N(7.0) software. Because of incorrect exception handling and an unprotected intent, AudioService can cause a system crash, The Samsung IDs are SVE-2017-8114, SVE-2017-8116, and SVE-2017-8117 (March 2017).

An issue was discovered on Samsung mobile devices with KK(4.4), L(5.0/5.1), M(6.0), and N(7.x) software. An attacker can crash system processes via a Serializable object because of missing exception handling. The Samsung IDs are SVE-2017-8109, SVE-2017-8110, SVE-2017-8115, SVE-2017-8118, and SVE-2017-8119 (April 2017).

An issue was discovered on Samsung mobile devices with L(5.0/5.1), M(6.0), and N(7.x) software. Intents related to Wi-Fi have incorrect exception handling, leading to a crash of system processes. The Samsung ID is SVE-2017-8389 (May 2017).

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 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).

An issue was discovered on Samsung mobile devices with N(7.x) software. Because of missing Intent exception handling, system_server can have a NullPointerException with a crash of a system process. The Samsung IDs are SVE-2017-9122, SVE-2017-9123, SVE-2017-9124, and SVE-2017-9126 (July 2017).

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 ril, there is a possible system crash due to an incorrect bounds check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07257259; Issue ID: ALPS07257259.

A double-free vulnerability exists in WebKit in Google Chrome before Blink M12 in the WebCore::CSSSelector function.

A lack of replay attack protection in Security Mode Command process prior to SMR Oct-2021 Release 1 can lead to denial of service on mobile network connection and battery depletion.

TensorFlow is an open source platform for machine learning. The `RaggedRangOp` function takes an argument `limits` that is eventually used to construct a `TensorShape` as an `int64`. If `limits` is a very large float, it can overflow when converted to an `int64`. This triggers an `InvalidArgument` but also throws an abort signal that crashes the program. We have patched the issue in GitHub commit 37cefa91bee4eace55715eeef43720b958a01192. 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 `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 `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. When `mlir::tfg::GraphDefImporter::ConvertNodeDef` tries to convert NodeDefs without an op name, it crashes. We have patched the issue in GitHub commit a0f0b9a21c9270930457095092f558fbad4c03e5. 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.

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. If `LowerBound` or `UpperBound` is given an empty`sorted_inputs` input, it results in a `nullptr` dereference, leading to a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bce3717eaef4f769019fd18e990464ca4a2efeea. 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.

In wlan firmware, there is possible system crash due to an uncaught exception. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07664720; Issue ID: ALPS07664720.

There exists a Denial of service vulnerability in Tink-cc in versions prior to 2.1.3.  * An adversary can crash binaries using the crypto::tink::JsonKeysetReader in tink-cc by providing an input that is not an encoded JSON object, but still a valid encoded JSON element, for example a number or an array. This will crash as Tink just assumes any valid JSON input will contain an object. * An adversary can crash binaries using the crypto::tink::JsonKeysetReader in tink-cc by providing an input containing many nested JSON objects. This may result in a stack overflow. We recommend upgrading to version 2.1.3 or above

A lack of replay attack protection in GUTI REALLOCATION COMMAND message process in Qualcomm modem prior to SMR Oct-2021 Release 1 can lead to remote denial of service on mobile network connection.

In btif_in_hf_client_generic_evt of btif_hf_client.cc, there is a possible Bluetooth service crash due to a missing null 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-12Android ID: A-180420059

In isWordBreakAfter of LayoutUtils.cpp, there is a possible way to slow or crash a TextView 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. Product: Android; Versions: Android-9, Android-10, Android-11, Android-8.0, Android-8.1; Android ID: A-170968514.

Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that `SafeToRemoveIdentity` would trigger `CHECK` failures. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that any binary op would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If `Tin` and `Tout` don't match the type of data in `out` and `input_*` tensors then `flat<*>` would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a `CHECK` crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that `IsSimplifiableReshape` would trigger `CHECK` failures. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. An attacker can trigger denial of service via assertion failure by altering a `SavedModel` on disk such that `AttrDef`s of some operation are duplicated. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the `DCHECK` function however, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the `ValueOrDie` line. This results in an assertion failure as `ret` contains an error `Status`, not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. Multiple operations in TensorFlow can be used to trigger a denial of service via `CHECK`-fails (i.e., assertion failures). This is similar to TFSA-2021-198 and has similar fixes. We have patched the reported issues in multiple GitHub commits. It is possible that other similar instances exist in TensorFlow, we will issue fixes as these are discovered. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that `TensorByteSize` would trigger `CHECK` failures. `TensorShape` constructor throws a `CHECK`-fail if shape is partial or has a number of elements that would overflow the size of an `int`. The `PartialTensorShape` constructor instead does not cause a `CHECK`-abort if the shape is partial, which is exactly what this function needs to be able to return `-1`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Google Chrome before 6.0.472.59 on Linux does not properly handle cursors, which might allow attackers to cause a denial of service (assertion failure) via unspecified vectors.

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 Bluetooth FW, there is a possible reachable assertion due to improper exception handling. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: WCNCR00389046 (Note: For MT79XX chipsets) / ALPS09136501 (Note: For MT2737, MT3603, MT6XXX, and MT8XXX chipsets); Issue ID: MSV-1797.

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."

In wlan STA driver, there is a possible reachable assertion due to improper exception handling. This could lead to local denial of service if a malicious actor has already obtained the System privilege. User interaction is not needed for exploitation. Patch ID: WCNCR00389047 / ALPS09136505; Issue ID: MSV-1798.