In BuildDevIDResponse of miscdatabuilder.cpp, there is a possible out of bounds write due to a missing bounds check. This could lead to remote code execution with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-229621649References: N/A

Heap buffer overflow in Visuals in Google Chrome prior to 112.0.5615.49 allowed a remote attacker who had compromised the renderer process to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

In Bluetooth, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege if more than 100 bluetooth devices have been connected with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-201083240

Out of bounds memory access in DOM Bindings in Google Chrome prior to 112.0.5615.49 allowed a remote attacker to perform out of bounds memory access via a crafted HTML page. (Chromium security severity: Medium)

In mtee, 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: ALPS07571485; Issue ID: ALPS07571485.

In usb, 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: ALPS07628505; Issue ID: ALPS07628505.

In wlan, 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: ALPS07796900; Issue ID: ALPS07796900.

In audio, 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: ALPS07648710; Issue ID: ALPS07648710.

In wlan, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07573480; Issue ID: ALPS07573480.

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)

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.

Heap buffer overflow in Video in Google Chrome prior to 110.0.5481.177 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

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)

In availableToWriteBytes of MessageQueueBase.h, 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.

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-2016-0966, CVE-2016-0967, CVE-2016-0968, CVE-2016-0969, 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 SwiftShader in Google Chrome prior to 117.0.5938.62 allowed a remote attacker to perform an out of bounds memory write via a crafted HTML page. (Chromium security severity: High)

Use after free in extensions in Google Chrome prior to 87.0.4280.88 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in clipboard in Google Chrome prior to 87.0.4280.66 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page.

Insufficient data validation in WASM in Google Chrome prior to 87.0.4280.66 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Stack buffer overflow in WebRTC in Google Chrome prior to 86.0.4240.183 allowed a remote attacker to potentially exploit stack corruption via a crafted WebRTC packet.

Use after free in WebXR in Google Chrome on Android prior to 86.0.4240.75 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Insufficient policy enforcement in ANGLE in Google Chrome prior to 86.0.4240.183 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in Freetype in Google Chrome prior to 86.0.4240.111 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in UI in Google Chrome on Android prior to 86.0.4240.185 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page.

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

Use after free in printing in Google Chrome prior to 86.0.4240.111 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in WebRTC in Google Chrome prior to 87.0.4280.66 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Inappropriate implementation in V8 in Google Chrome prior to 86.0.4240.183 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Insufficient data validation in media in Google Chrome prior to 85.0.4183.121 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Inappropriate implementation in V8 in Google Chrome prior to 86.0.4240.183 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Use after free in clipboard in Google Chrome prior to 87.0.4280.88 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Use after free in WebCodecs in Google Chrome prior to 87.0.4280.66 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Use after free in audio in Google Chrome prior to 86.0.4240.75 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

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)

Heap buffer overflow in libphonenumber in Google Chrome prior to 109.0.5414.74 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Low)

Use after free in WebRTC in Google Chrome prior to 86.0.4240.75 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in Blink in Google Chrome prior to 101.0.4951.41 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: High)

Inappropriate implementation in Blink in Google Chrome prior to 86.0.4240.111 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Use after free in PDFium in Google Chrome prior to 86.0.4240.111 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.

In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Use after free in WebRTC in Google Chrome prior to 87.0.4280.66 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Insufficient data validation in V8 in Google Chrome prior to 87.0.4280.88 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Inappropriate implementation in V8 in Google Chrome prior to 86.0.4240.75 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Inappropriate implementation in V8 in Google Chrome prior to 86.0.4240.198 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Use after free in media in Google Chrome prior to 86.0.4240.111 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.