In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, when an audio driver ioctl handler is called, a kernel out-of-bounds write can potentially occur.

In android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, when processing a specially crafted QCA_NL80211_VENDOR_SUBCMD_ENCRYPTION_TEST cfg80211 vendor command a stack-based buffer overflow can occur.

In nfc_ncif_proc_get_routing of nfc_ncif.cc in Android-7.0, Android-7.1.1, Android-7.1.2, Android-8.0, Android-8.1 and Android-9, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Android ID: A-117554809.

In nfc_ncif_set_config_status of nfc_ncif.cc in Android-7.0, Android-7.1.1, Android-7.1.2, Android-8.0, Android-8.1 and Android-9, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Android ID: A-114047681.

In HID_DevAddRecord of hidd_api.cc, there is a possible out-of-bounds write due to a missing bounds check. This could lead to local escalation of privilege in the Bluetooth service with User execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android-9. Android ID: A-79946737.

In BTA_HdRegisterApp of bta_hd_api.cc, there is a possible out-of-bound write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android-9. Android ID: A-113111784

TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of `ArgMin`/`ArgMax`(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/arg_min_max.cc#L52-L59). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the condition in the `if` is never true, so code writes past the last valid element of `output_dims->data`. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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.

TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset. 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 driver_override_store of bus.c, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Product: Android Versions: Android kernel Android ID: A-74128061 References: Upstream kernel.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. 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.

TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. 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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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 persist_set_key and other functions of cryptfs.cpp, there is a possible out-of-bounds write due to an uncaught error. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android-7.0 Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android-9. Android ID: A-112731440.

An improper length check in APAService prior to SMR Sep-2021 Release 1 results in stack based Buffer Overflow.

A possible stack-based buffer overflow vulnerability in Widevine trustlet prior to SMR Oct-2021 Release 1 allows arbitrary code execution.

An improper boundary check in secure_log of LDFW and BL31 prior to SMR Dec-2021 Release 1 allows arbitrary memory write and code execution.

A possible out of bounds write vulnerability in NPU driver prior to SMR JUN-2021 Release 1 allows arbitrary memory write.

An improper input validation vulnerability in NPU firmware prior to SMR MAY-2021 Release 1 allows arbitrary memory write and code execution.

A possible buffer overflow vulnerability in NPU driver prior to SMR JUN-2021 Release 1 allows arbitrary memory write and code execution.

In apusys, there is a possible memory corruption due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05672107; Issue ID: ALPS05687781.

In __hidinput_change_resolution_multipliers of hid-input.c, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-173843328References: Upstream kernel

In clk driver, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05479659; Issue ID: ALPS05479659.

In parsePrimaryFieldFirstUidAnnotation of LogEvent.cpp, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11Android ID: A-174485572

In sound_trigger_event_alloc of platform.h, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-167663878

In ReadLogicalParts of basicmbr.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android; Versions: Android-8.1, Android-9, Android-10, Android-11, Android-8.0; Android ID: A-158063095.

In ged, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Product: Android; Versions: Android-8.1, Android-9, Android-10, Android-11; Patch ID: ALPS05431161.

In decrypt_1_2 of CryptoPlugin.cpp, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-9 Android-10 Android-11 Android-8.1Android ID: A-176444622

In noteAtomLogged of StatsdStats.cpp, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-9Android ID: A-187957589

In apusys, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05672107; Issue ID: ALPS05672003.

In apusys, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05672107; Issue ID: ALPS05672107.

In ged, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05687510; Issue ID: ALPS05687510.

In sqlite3_str_vappendf of sqlite3.c, there is a possible out of bounds write due to improper input validation. This could lead to local escalation of privilege if the user can also inject a printf into a privileged process's SQL with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-9 Android-10 Android-11 Android-8.1Android ID: A-153352319

An arbitrary memory overwrite vulnerability in Asylo versions up to 0.6.0 allow an attacker to make an Ecall_restore function call to reallocate untrusted code and overwrite sections of the Enclave memory address. We recommend updating your library.

In WLAN driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06704526; Issue ID: ALPS06704482.

In WLAN driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06493873; Issue ID: ALPS06493899.

In OMA DRM, there is a possible memory corruption due to improper input validation. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05722454; Issue ID: ALPS05722454.

In apusys, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05672107; Issue ID: ALPS05672038.

In imgsensor, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06479763; Issue ID: ALPS06479763.

A stack-based buffer overflow can occur in fastboot from all Android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the Linux kernel.

In usim_SendMCCMNCIndMsg of usim_Registration.c, there is a possible out of bounds write due to memory corruption. This could lead to physical escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In iaxxx_calc_i2s_div of iaxxx-codec.c, there is a possible hardware port write with user controlled data due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-180950209

In apusys, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05672107; Issue ID: ALPS05722511.

In apusys, there is a possible out of bounds write due to a stack-based buffer overflow. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05672103; Issue ID: ALPS05672103.