Heap buffer overflow in Autofill in Google Chrome on Android prior to 91.0.4472.77 allowed a remote attacker to perform out of bounds memory access via a crafted HTML page.

Stack buffer overflow in Printing in Google Chrome prior to 92.0.4515.107 allowed a remote attacker who had compromised the renderer process to potentially exploit stack corruption via a crafted HTML page.

Out of bounds write in Tab Groups in Google Chrome on Linux and ChromeOS prior to 92.0.4515.107 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 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.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, a buffer over-read can occur In the WMA NDP event handler functions due to lack of validation of input value event_info which is received from FW.

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.

In all android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, Buffer overread may occur due to non-null terminated strings while processing vsprintf in camera jpeg driver.

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.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, in wma_ndp_confirm_event_handler and wma_ndp_indication_event_handler, ndp_cfg len and num_ndp_app_info is from fw. If they are not checked, it may cause buffer over-read once the value is too large.

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.

On affected platforms running Arista CloudEOS an issue in the Software Forwarding Engine (Sfe) can lead to a potential denial of service attack by sending malformed packets to the switch. This causes a leak of packet buffers and if enough malformed packets are received, the switch may eventually stop forwarding traffic.

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)

TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`expand_dims.cc`](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If `axis` is a large negative value (e.g., `-100000`), then after the first `if` it would still be negative. The check following the `if` statement will pass and the `for` loop would read one element before the start of `input_dims.data` (when `i = 0`). We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. 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.

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.

Out of bounds read in WebRTC in Google Chrome prior to 110.0.5481.77 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: High)

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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.

Out of bounds read in V8 API in Google Chrome prior to 124.0.6367.78 allowed a remote attacker to leak cross-site data via a crafted HTML page. (Chromium security severity: High)

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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

In wifi driver, 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: ALPS05551435; Issue ID: ALPS05551435.

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 wifi driver, there is a possible out of bounds read due to a missing bounds check. This could lead to remote information disclosure to a proximal attacker with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android SoCAndroid ID: A-187149601

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 WT_InterpolateNoLoop of eas_wtengine.c, there is a possible out of bounds read due to an incorrect bounds check. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-9Android ID: A-190286685

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.

In asf extractor, there is a possible out of bounds read due to an incorrect bounds check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05495528; Issue ID: ALPS05495528.

In getUpTo17bits of pvmp3_getbits.cpp, there is a possible out of bounds read 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-11Android ID: A-154075955

In asf extractor, there is a possible out of bounds read due to an incorrect bounds check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05489178; Issue ID: ALPS05489178.