Out of bounds memory access in ANGLE in Google Chrome prior to 93.0.4577.82 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in Media in Google Chrome on Linux prior to 88.0.4324.182 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while processing a message from firmware in WLAN handler, a buffer overwrite can occur.

Heap buffer overflow in tab groups in Google Chrome prior to 89.0.4389.90 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, lack of check on input received to calculate the buffer length can lead to out of bound write to kernel stack.

Heap buffer overflow in V8 in Google Chrome prior to 88.0.4324.150 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in Reader Mode in Google Chrome prior to 90.0.4430.212 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Out of bounds write in Tab Groups in Google Chrome prior to 92.0.4515.131 allowed an attacker who convinced a user to install a malicious extension to perform an out of bounds memory write via a crafted HTML page.

Out of bounds write in ANGLE in Google Chrome prior to 91.0.4472.101 allowed a remote attacker to potentially perform out of bounds memory access via a crafted HTML page.

Out of bounds write in TabStrip in Google Chrome prior to 91.0.4472.77 allowed an attacker who convinced a user to install a malicious extension to perform an out of bounds memory write via a crafted HTML page.

Adobe Flash Player before 18.0.0.329 and 19.x and 20.x before 20.0.0.306 on Windows and OS X and before 11.2.202.569 on Linux, Adobe AIR before 20.0.0.260, Adobe AIR SDK before 20.0.0.260, and Adobe AIR SDK & Compiler before 20.0.0.260 allow attackers to execute arbitrary code or cause a denial of service (memory corruption) via unspecified vectors, a different vulnerability than CVE-2016-0964, CVE-2016-0965, CVE-2016-0967, CVE-2016-0968, CVE-2016-0969, CVE-2016-0970, CVE-2016-0972, CVE-2016-0976, CVE-2016-0977, CVE-2016-0978, CVE-2016-0979, CVE-2016-0980, and CVE-2016-0981.

Out of bounds write in V8 in Google Chrome prior to 93.0.4577.82 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Stack buffer overflow in GPU Process in Google Chrome on Linux prior to 88.0.4324.182 allowed a remote attacker to potentially perform out of bounds memory access via a crafted HTML page.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, improper check In the WMA API for the inputs received from the firmware and then fills the same to the host structure will lead to OOB write.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, WMA handler carries a fixed event data from the firmware to the host . If the length and anqp length from this event data exceeds the max length, an OOB write would happen.

Heap buffer overflow in Blink in Google Chrome prior to 88.0.4324.96 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in History in Google Chrome prior to 90.0.4430.212 allowed a remote attacker who had compromised the renderer process to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in TabStrip in Google Chrome prior to 89.0.4389.114 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Stack buffer overflow in ANGLE in Google Chrome prior to 93.0.4577.82 allowed a remote attacker to potentially exploit stack corruption via a crafted HTML page.

Heap buffer overflow in Web Audio API in Google Chrome prior to 111.0.5563.64 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

Heap buffer overflow in WebXR in Google Chrome prior to 91.0.4472.164 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. 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.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service. The implementation(https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid. Negative values for `seq_dim` can result in stack overflow or `CHECK`-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of `batch_dim`. 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. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 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 HTBLogKM of TBD, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android SoCAndroid ID: A-242345178

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.

In StreamOut::prepareForWriting of StreamOut.cpp, there is a possible out of bounds write due to a use after free. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-8.1 Android-9 Android-10 Android-11Android ID: A-185259758

In MMU_MapPages of TBD, there is a possible out of bounds write 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.Product: AndroidVersions: Android SoCAndroid ID: A-238916921

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.

Heap buffer overflow in Platform Apps in Google Chrome on Chrome OS prior to 109.0.5414.74 allowed an attacker who convinced a user to install a malicious extension to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

In phNxpNciHal_process_ext_rsp of phNxpNciHal_ext.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to remote code execution over NFC with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-9Android ID: A-181660091

In _PMRCreate of the PowerVR kernel driver, a missing bounds check means it is possible to overwrite heap memory via PhysmemNewRamBackedPMR. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In ih264d_mark_err_slice_skip of ih264d_parse_pslice.c, there is a possible out of bounds write due to a heap buffer overflow. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is needed for exploitation.Product: AndroidVersions: Android-9 Android-10 Android-11 Android-8.1Android ID: A-182152757

In Scanner::LiteralBuffer::NewCapacity of scanner.cc, there is a possible out of bounds write due to an integer overflow. This could lead to remote code execution if an attacker can supply a malicious PAC file, with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-8.1 Android-9 Android-10Android ID: A-168041375

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.

In __host_check_page_state_range of mem_protect.c, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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.

In BITSTREAM_FLUSH of ih264e_bitstream.h, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-8.1 Android-9Android ID: A-176533109

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`. 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 avrc_msg_cback of avrc_api.cc, there is a possible out of bounds write due to a heap buffer overflow. This could lead to remote code execution with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-8.1 Android-9 Android-10Android ID: A-177611958

In rw_i93_send_to_lower of rw_i93.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: AndroidVersions: Android-11Android ID: A-157650357

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 MMU_UnmapPages of the PowerVR kernel driver, 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 SoCAndroid ID: A-243825200

In various functions in WideVine, there are possible out of bounds writes due to improper input validation. This could lead to remote code execution with no additional execution privileges needed. User interaction is needed for exploitation.Product: AndroidVersions: Android SoCAndroid ID: A-188061006

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 parseExclusiveStateAnnotation 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-174488848

TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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.

Heap buffer overflow in Metrics in Google Chrome prior to 111.0.5563.64 allowed a remote attacker who had compromised the renderer process to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)