TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer. The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.

Out of bounds read in ANGLE in Google Chrome prior to 99.0.4844.51 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Out of bounds read in audio in Google Chrome prior to 86.0.4240.75 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page.

Out of bounds read in networking in Google Chrome prior to 87.0.4280.88 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Out of bounds read in ANGLE allowed a remote attacker to obtain sensitive data via a crafted HTML page.

In NFC, 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.Product: AndroidVersions: Android-11Android ID: A-139188779

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

In btm_proc_sp_req_evt of btm_sec.cc, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure via compromised device firmware with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10Android ID: A-142543497

In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

Out of bounds memory access in CSS in Google Chrome prior to 116.0.5845.110 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: High)

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.

In CreateDeviceInfo of trusty_remote_provisioning_context.cpp, 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.Product: AndroidVersions: Android kernelAndroid ID: A-193579873References: N/A

An issue was discovered on Samsung mobile devices with P(9.0) (Exynos chipsets) software. Kernel Wi-Fi drivers allow out-of-bounds Read or Write operations (e.g., a buffer overflow). The Samsung IDs are SVE-2019-16125, SVE-2019-16134, SVE-2019-16158, SVE-2019-16159, SVE-2019-16319, SVE-2019-16320, SVE-2019-16337, SVE-2019-16464, SVE-2019-16465, SVE-2019-16467 (March 2020).

Skia, as used in Google Chrome before 25.0.1364.97 on Windows and Linux, and before 25.0.1364.99 on Mac OS X, allows remote attackers to cause a denial of service (out-of-bounds read) via vectors related to a "user gesture check for dangerous file downloads."

TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `SparseCountSparseOutput` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while processing the function for writing device values into flash, uninitialized memory can be written to flash.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, if userspace provides a too-large IE length in wlan_hdd_cfg80211_set_ie, a buffer over-read occurs.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while processing a gpt update, an out of bounds memory access may potentially occur.

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `values`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Array bounds check failure in V8 in Google Chrome prior to 67.0.3396.62 allowed a remote attacker to perform an out of bounds memory read via a crafted PDF file.

In Android releases from CAF using the linux kernel (Android for MSM, Firefox OS for MSM, QRD Android) before security patch level 2018-06-05, kernel panic may happen due to out-of-bound read, caused by not checking source buffer length against length of packet stream to be copied.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, while parsing Netlink attributes, a buffer overread can occur.

In Qualcomm Android for MSM, Firefox OS for MSM, and QRD Android with all Android releases from CAF using the Linux kernel before security patch level 2018-04-05, a policy for the packet pattern attribute NL80211_PKTPAT_OFFSET is not defined which can lead to a buffer over-read in nla_get_u32().

In all Android releases from CAF using the Linux kernel, an out of bounds access can potentially occur in a camera function.

In NFC, there is a possible out of bounds read due to a missing bounds check. This could lead to remote information disclosure. System execution privileges, a Firmware compromise, and User interaction are needed for exploitation.Product: AndroidVersions: Android-11Android ID: A-144506224

TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

In all Qualcomm products with Android releases from CAF using the Linux kernel, array out of bounds access can occur if userspace sends more than 16 multicast addresses.

In all Qualcomm products with Android releases from CAF using the Linux kernel, the camera application can possibly request frame/command buffer processing with invalid values leading to the driver performing a heap buffer over-read.

In Gnss service, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

A memory corruption bug in WebAssembly could lead to out of bounds read and write through V8 in WebAssembly in Google Chrome prior to 62.0.3202.62 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for the `QuantizeAndDequantizeV*` operations can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, while processing a specially crafted cfg80211 vendor command, a buffer over-read can occur.

An array out-of-bounds access in all Qualcomm products with Android releases from CAF using the Linux kernel can potentially occur in a camera driver.

In video service, there is a possible out of bounds read due to a incorrect bounds check. This could lead to local denial of service with no additional execution privileges needed

Adobe Flash Player versions 29.0.0.171 and earlier have an Out-of-bounds read vulnerability. Successful exploitation could lead to information disclosure.

While flashing a meta image, a buffer over-read can potentially occur when the number of images are out of the maximum range of 32 in Android releases from CAF using the linux kernel (Android for MSM, Firefox OS for MSM, QRD Android) before security patch level 2018-06-05.

In camera service, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, while processing the SENDACTIONFRAME IOCTL, a buffer over-read can occur if the payload length is less than 7.

In all Android releases from CAF using the Linux kernel, a buffer overread can occur if a particular string is not NULL terminated.

In gpu driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In avrc_ctrl_pars_vendor_rsp of avrc_pars_ct.cc, there is a possible out of bounds read due to a missing bounds check. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-12LAndroid ID: A-205837191

In video service, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed

In gsp driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In bnep_data_ind of bnep_main.cc, there is a possible out of bounds read due to a missing bounds check. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: 5.1.1, 6.0, 6.0.1, 7.0, 7.1.1, 7.1.2, 8.0, 8.1. Android ID: A-67863755.

In functionality implemented in sdp_discovery.cc, there are possible out of bounds reads due to missing bounds checks. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: 5.1.1, 6.0, 6.0.1, 7.0, 7.1.1, 7.1.2, 8.0, 8.1. Android ID: A-68161546.

The compositor in Google Chrome before 22.0.1229.92 allows remote attackers to cause a denial of service (out-of-bounds read) via unspecified vectors.

The International Components for Unicode (ICU) functionality in Google Chrome before 22.0.1229.92 allows remote attackers to cause a denial of service (out-of-bounds read) via vectors related to a regular expression.