Inappropriate implementation in V8 in Google Chrome prior to 129.0.6668.70 allowed a remote attacker to potentially perform out of bounds memory access via a crafted HTML page. (Chromium security severity: High)

In libFDK, there is a possible out of bounds write due to an integer overflow. This could lead to remote code execution with no additional execution privileges needed. User interaction is needed for exploitation. Product: AndroidVersions: Android-10Android ID: A-112891546

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.

Heap buffer overflow in WebGL in Google Chrome prior to 92.0.4515.107 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Out of bounds write in Autofill in Google Chrome prior to 92.0.4515.107 allowed a remote attacker who had compromised the renderer process to potentially exploit heap corruption via a crafted HTML page.

An integer overflow in xmlmemory.c in libxml2 before 2.9.5, as used in Google Chrome prior to 62.0.3202.62 and other products, allowed a remote attacker to potentially exploit heap corruption via a crafted XML file.

Out of bounds write in Tab Strip in Google Chrome prior to 90.0.4430.212 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 and a crafted Chrome extension.

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.

Inappropriate implementation in V8 in Google Chrome prior to 129.0.6668.58 allowed a remote attacker to potentially exploit stack corruption via a crafted HTML page. (Chromium security severity: Medium)

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 V8 in Google Chrome prior to 93.0.4577.82 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 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. 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.

Heap buffer overflow in Layout in Google Chrome prior to 127.0.6533.99 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

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.

Array index error in the FEBlend::apply function in WebCore/platform/graphics/filters/FEBlend.cpp in WebKit, as used in Google Chrome before 7.0.517.44, webkitgtk before 1.2.6, and other products, allows remote attackers to cause a denial of service and possibly execute arbitrary code via a crafted SVG document, related to effects in the application of filters.

Heap buffer overflow in Skia in Google Chrome prior to 128.0.6613.113 allowed a remote attacker who had compromised the renderer process to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Heap buffer overflow in PDFium in Google Chrome prior to 128.0.6613.84 allowed a remote attacker to perform an out of bounds memory read via a crafted PDF file. (Chromium security severity: Medium)

Inappropriate implementation in V8 in Google Chrome prior to 128.0.6613.84 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Out of bounds write in V8 in Google Chrome prior to 128.0.6613.119 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

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 Skia in Google Chrome prior to 128.0.6613.113 allowed a remote attacker who had compromised the renderer process to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Heap buffer overflow in Fonts in Google Chrome prior to 128.0.6613.84 allowed a remote attacker to potentially exploit heap corruption 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.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 by passing crafted inputs to `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements. If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. 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.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.

Inappropriate implementation in V8 in Google Chrome prior to 126.0.6478.182 allowed a remote attacker to perform out of bounds memory access 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.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. 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.

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)

Adobe Flash Player versions 25.0.0.148 and earlier have an exploitable memory corruption vulnerability in the Graphics class. Successful exploitation could lead to arbitrary code execution.

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.

Adobe Flash Player versions 24.0.0.221 and earlier have an exploitable memory corruption vulnerability in the Primetime TVSDK API functionality related to timeline interactions. Successful exploitation could lead to arbitrary code execution.

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.

Any project that parses untrusted Protocol Buffers data containing an arbitrary number of nested groups / series of SGROUP tags can corrupted by exceeding the stack limit i.e. StackOverflow. Parsing nested groups as unknown fields with DiscardUnknownFieldsParser or Java Protobuf Lite parser, or against Protobuf map fields, creates unbounded recursions that can be abused by an attacker.

Inappropriate implementation in V8 in Google Chrome prior to 126.0.6478.182 allowed a remote attacker to potentially exploit heap corruption 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 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.

Out of bounds memory access in V8 in Google Chrome prior to 126.0.6478.182 allowed a remote attacker to potentially perform a sandbox escape 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.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 PDF in Google Chrome prior to 124.0.6367.78 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file. (Chromium security severity: Medium)

Inappropriate implementation in V8 in Google Chrome prior to 126.0.6478.54 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Low)

In multiple locations of the nanopb library, there is a possible way to corrupt memory when decoding untrusted protobuf files. This could lead to local escalation of privilege,with no additional execution privileges needed. User interaction is not needed for exploitation.

Use after free in Blink in Google Chrome prior to 74.0.3729.108 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Use after free in PDFium in Google Chrome prior to 76.0.3809.100 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.

Use after free in Blink in Google Chrome prior to 75.0.3770.90 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in Layout in Google Chrome prior to 127.0.6533.72 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

Incorrect optimization assumptions in V8 in Google Chrome prior to 72.0.3626.81 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page.

Inappropriate implementation in JavaScript in Google Chrome prior to 76.0.3809.87 allowed a remote attacker to potentially exploit object corruption via a crafted HTML page.

Out of bounds memory access in JavaScript in Google Chrome prior to 75.0.3770.80 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Integer overflow in PDFium in Google Chrome prior to 76.0.3809.87 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.