TensorFlow is an open source platform for machine learning. When `AudioSummaryV2` receives an input `sample_rate` with more than one element, it gives a `CHECK` fails that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bf6b45244992e2ee543c258e519489659c99fb7f. 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. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the `tf.compat.v1.signal.rfft2d` and `tf.compat.v1.signal.rfft3d` lack input validation and under certain condition can result in crashes (due to `CHECK`-failures). Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

In Modem 4G RRC, there is a possible system crash due to improper input validation. This could lead to remote denial of service, when concatenating improper SIB12 (CMAS message), with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY00867883; Issue ID: ALPS07274118.

grpc-swift is the Swift language implementation of gRPC, a remote procedure call (RPC) framework. Prior to version 1.7.2, a grpc-swift server is vulnerable to a denial of service attack via a reachable assertion. This is due to incorrect logic when handling GOAWAY frames. The attack is low-effort: it takes very little resources to construct and send the required sequence of frames. The impact on availability is high as the server will crash, dropping all in flight connections and requests. This issue is fixed in version 1.7.2. There are currently no known workarounds.

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.

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. 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. 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. 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. 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 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. 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. 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. 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 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.

In Bluetooth firmware, there is a possible firmware asssert due to improper handling of exceptional conditions. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS09001270; Issue ID: MSV-1600.

In Modem, there is a possible system crash 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. Patch ID: MOLY00843282; Issue ID: MSV-1535.

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.

User process can perform the kernel DOS in ashmem when doing cache maintenance operation in all Android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the Linux kernel.

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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks. 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.

In MPD before 0.23.8, as used on Automotive Grade Linux and other platforms, the PipeWire output plugin mishandles a Drain call in certain situations involving truncated files. Eventually there is an assertion failure in libmpdclient because libqtappfw passes in a NULL pointer.

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 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 5G Modem, there is a possible system crash due to improper error handling. This could lead to remote denial of service when receiving malformed RRC messages, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01128524; Issue ID: MOLY01138453 (MSV-861).

In 5G Modem, there is a possible system crash due to improper error handling. This could lead to remote denial of service when receiving malformed RRC messages, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01128524; Issue ID: MOLY01128524 (MSV-846).

In 5G Modem, there is a possible system crash due to improper error handling. This could lead to remote denial of service when receiving malformed RRC messages, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01128524; Issue ID: MOLY01130183 (MSV-850).

In wlan firmware, there is a possible firmware assertion due to improper input handling. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07932637; Issue ID: ALPS07932637.

In 5G Modem, there is a possible system crash due to improper error handling. This could lead to remote denial of service when receiving malformed RRC messages, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01130204; Issue ID: MOLY01130204 (MSV-849).

Rekor's goals are to provide an immutable tamper resistant ledger of metadata generated within a software projects supply chain. A malformed proposed entry of the `intoto/v0.0.2` type can cause a panic on a thread within the Rekor process. The thread is recovered so the client receives a 500 error message and service still continues, so the availability impact of this is minimal. This has been fixed in v1.2.0 of Rekor. Users are advised to upgrade. There are no known workarounds for this vulnerability.

In 5G Modem, there is a possible system crash due to improper error handling. This could lead to remote denial of service when receiving malformed RRC messages, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01128524; Issue ID: MOLY01139296 (MSV-860).

In 5G Modem, there is a possible system crash due to improper error handling. This could lead to remote denial of service when receiving malformed RRC messages, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01130256; Issue ID: MOLY01130256 (MSV-848).

In Modem, there is a possible system crash due to incorrect error handling. This could lead to remote denial of service, if a UE has connected to a rogue base station controlled by the attacker, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01753620; Issue ID: MSV-6100.

In the Android kernel in the video driver there is a kernel pointer leak due to a WARN_ON statement. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions providing a negative element to `num_elements` list argument of `tf.raw_ops.TensorListReserve` causes the runtime to abort the process due to reallocating a `std::vector` to have a negative number of elements. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls `std::vector.resize()` with the new size controlled by input given by the user, without checking that this input is valid. We have patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2. 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.

cordova-plugin-fingerprint-aio is a plugin provides a single and simple interface for accessing fingerprint APIs on both Android 6+ and iOS. In versions prior to 5.0.1 The exported activity `de.niklasmerz.cordova.biometric.BiometricActivity` can cause the app to crash. This vulnerability occurred because the activity didn't handle the case where it is requested with invalid or empty data which results in a crash. Any third party app can constantly call this activity with no permission. A 3rd party app/attacker using event listener can continually stop the app from working and make the victim unable to open it. Version 5.0.1 of the cordova-plugin-fingerprint-aio doesn't export the activity anymore and is no longer vulnerable. If you want to fix older versions change the attribute android:exported in plugin.xml to false. Please upgrade to version 5.0.1 as soon as possible.

In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error.

In Modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service, if a UE has connected to a rogue base station controlled by the attacker, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01717526; Issue ID: MSV-5591.

In Modem, there is a possible system crash due to incorrect error handling. This could lead to remote denial of service, if a UE has connected to a rogue base station controlled by the attacker, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01685181; Issue ID: MSV-4760.

In Modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service, if a UE has connected to a rogue base station controlled by the attacker, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY00827332; Issue ID: MSV-5919.

In Modem, there is a possible system crash due to a missing bounds check. This could lead to remote denial of service, if a UE has connected to a rogue base station controlled by the attacker, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01688495; Issue ID: MSV-4818.

In Modem, there is a possible system crash due to an uncaught exception. This could lead to remote denial of service, if a UE has connected to a rogue base station controlled by the attacker, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01738310; Issue ID: MSV-5933.

In Modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service, if a UE has connected to a rogue base station controlled by the attacker, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01673751; Issue ID: MSV-4644.