In keyinstall, there is a possible memory corruption due to a missing bounds check. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS08017756; Issue ID: ALPS07905323.
In list_key_entries of utils.rs, there is a possible way to disable user credentials due to resource exhaustion. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-222287335
In apu, there is a possible memory corruption due to a missing bounds check. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07767818; Issue ID: ALPS07767818.
In cmdq, there is a possible out of bounds read due to an incorrect status check. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS08021592; Issue ID: ALPS08021592.
In power, there is a possible memory corruption due to an incorrect bounds check. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07929790; Issue ID: ALPS07929790.
In cmdq, there is a possible memory corruption due to a missing bounds check. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07636133; Issue ID: ALPS07636130.
In vcu, there is a possible memory corruption due to type confusion. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07519103; Issue ID: ALPS07519121.
In cmdq, there is a possible memory corruption due to a missing bounds check. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07636133; Issue ID: ALPS07634601.
In modem control device, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In cp_dump driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In log service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In modem control device, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In cp_dump driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
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 with System execution privileges needed
In camera driver, there is a possible out of bounds write due to a incorrect bounds check. This could lead to local denial of service with System execution privileges needed
In bluetooth service, there is a possible missing params check. This could lead to local denial of service with System execution privileges needed.
In camera driver, there is a possible use after free due to a logic error. This could lead to local denial of service with System execution privileges needed
In soter service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In tee service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In tee service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In FM service , there is a possible missing params check. This could lead to local denial of service in FM service .
In MP3 encoder, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
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 with System execution privileges needed.
In bootcp service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In ext4fsfilter driver, there is a possible out of bounds read due to a missing bounds check. This could local denial of service with System execution privileges needed.
In ext4fsfilter driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In soter service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In spipe drive, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In camera driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed
In ext4fsfilter driver, there is a possible out of bounds read due to a missing bounds check. This could local denial of service with System execution privileges needed.
In Image filter, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In modem control device, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In camera driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed
In soter service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In phasecheck server, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
In soter service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.
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 with System execution privileges needed.
In ged, there is a possible out of bounds write due to an integer overflow. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation Patch ID: ALPS07835901; Issue ID: ALPS07835901.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.UnravelIndex` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unravel_index_op.cc#L36) does not check that the tensor subsumed by `dims` is not empty. Hence, if one element of `dims` is 0, the implementation does a division by 0. We have patched the issue in GitHub commit a776040a5e7ebf76eeb7eb923bf1ae417dd4d233. 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 `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version.
TensorFlow is an end-to-end open source platform for machine learning. The code for `tf.raw_ops.UncompressElement` can be made to trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a `CompressedElement` from a `Variant` tensor and then proceeds to dereference it for decompressing. There is no check that the `Variant` tensor contained a `CompressedElement`, so the pointer is actually `nullptr`. We have patched the issue in GitHub commit 7bdf50bb4f5c54a4997c379092888546c97c3ebd. 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.
In isp, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed. User interaction is needed for exploitation. Patch ID: ALPS09071481; Issue ID: MSV-1730.
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.
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.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, `C.TF_TString_Dealloc` is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions). To fix this, the call to set the finalizer function is deferred until `NewTensor` returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written. We have patched the issue in GitHub commit 8721ba96e5760c229217b594f6d2ba332beedf22. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, which is the other affected version.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.ResourceScatterDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76. 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 craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). We have patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit 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 trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.MapStage`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the `key` input is a valid non-empty tensor. We have patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. 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 trigger a crash via a floating point exception in `tf.raw_ops.ResourceGather`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, `batch_size`, and then divides by it without checking that this value is not 0. We have patched the issue in GitHub commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11. 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 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.