In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122). An attacker can craft a model such that `ellipsis_end_idx` is smaller than `i` (e.g., always negative). In this case, the inner loop does not increase `i` and the `continue` statement causes execution to skip over the preincrement at the end of the outer loop. We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. TensorFlow 2.6.0 is the only affected version.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. 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 the implementation of division in TFLite is [vulnerable to a division by 0 error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. We have patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28. 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 a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992. 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 issue was discovered in Finder on Samsung mobile devices with Q(10.0) software. A call to a non-existent provider allows attackers to cause a denial of service. The Samsung ID is SVE-2020-18629 (December 2020).
In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.
TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `AllToAll` can be made to execute a division by 0. This occurs whenever the `split_count` argument is 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.
In wlan driver, there is a possible null pointer dereference issue due to a missing bounds check. This could lead to local denial of service in wlan services.
In modem, there is a possible missing verification of NAS Security Mode Command Replay Attacks in LTE. This could local denial of service with no additional execution privileges.
In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.
TensorFlow is an open source platform for machine learning. In affected versions if `tf.summary.create_file_writer` is called with non-scalar arguments code crashes due to a `CHECK`-fail. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.
In modem, there is a possible missing verification of HashMME value in Security Mode Command. This could local denial of service with no additional execution privileges.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In camera driver, there is a possible memory corruption due to improper locking. This could lead to local denial of service in kernel.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
An issue was discovered on Samsung mobile devices with software through 2016-05-27 (Exynos AP chipsets). A local graphics user can cause a Kernel Crash via the fb0(DECON) frame buffer interface. The Samsung ID is SVE-2016-7011 (October 2016).
In multiple locations, there is a possible permanent denial of service 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 multiple functions of DexUseManagerLocal.java, there is a possible way to crash system server due to a logic error in the code. This could lead to local permanent denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.
In multiple locations, there is a possible method for a malicious app to prevent dialing emergency services under limited circumstances due to a logic error in the code. This could lead to local denial of service until the phone reboots with no additional execution privileges needed. User interaction is not needed for exploitation.
In camera driver, there is a possible memory corruption due to improper locking. This could lead to local denial of service in kernel.
In camera driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sprd_sysdump driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In camera driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
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 macros that TensorFlow uses for writing assertions (e.g., `CHECK_LT`, `CHECK_GT`, etc.) have an incorrect logic when comparing `size_t` and `int` values. Due to type conversion rules, several of the macros would trigger incorrectly. 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.
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.UnsortedSegmentJoin` 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 `num_segments` is a scalar but there is no validation for this before accessing its value. 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.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.