In extract of MediaMetricsItem.h, there is a possible out of bounds read due to improper input validation. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12Android ID: A-204445255

In LoadedPackage::Load of LoadedArsc.cpp, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure when parsing an APK file with no additional execution privileges needed. User interaction is needed for exploitation.Product: AndroidVersions: Android-12Android ID: A-203938029

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 iaxxx_btp_write_words of iaxxx-btp.c, there is a possible out of bounds read due to an incorrect 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-198653629References: N/A

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

TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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.

TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. 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.

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

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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a `CHECK`-fail in debug builds of TensorFlow using `tf.raw_ops.ResourceGather` or a read from outside the bounds of heap allocated data in the same API in a release build. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the `batch_dims` value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of `tensor`, this results in reading data from outside the bounds of heap allocated buffer backing the tensor. We have patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d. 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.

Out of bounds read in libjpeg-turbo in Google Chrome prior to 94.0.4606.54 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In LogResponse of Dns.cpp, there is a possible out of bounds read 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.Product: AndroidVersions: Android-13Android ID: A-261079188

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

Out of bounds read in V8 in Google Chrome prior to 121.0.6167.139 allowed a remote attacker to potentially perform out of bounds memory access via a crafted HTML page. (Chromium security severity: Medium)

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. 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.

Out of bounds read in WebAudio in Google Chrome prior to 95.0.4638.54 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

An exploitable denial-of-service vulnerability exists in the Weave certificate loading functionality of Nest Cam IQ Indoor camera, version 4620002. A specially crafted weave packet can cause an integer overflow and an out-of-bounds read on unmapped memory to occur, resulting in a denial of service. An attacker can send a specially crafted packet to trigger.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. 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.

Out of bounds memory access in Blink Serial API in Google Chrome prior to 97.0.4692.71 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page and virtual serial port driver.

In update_freq_data of , there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.

NVIDIA driver contains a possible out-of-bounds read vulnerability due to a leak which may lead to information disclosure. This issue is rated as moderate. Android: A-63851980.

In ProtocolPsKeepAliveStatusAdapter::getCode() of protocolpsadapter.cpp, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure with baseband firmware compromise required. User interaction is not needed for exploitation.

In fvp_set_target of fvp.c, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.

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 SAEMM_RetrieveTaiList of SAEMM_ContextManagement.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 not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-240728187References: N/A

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. 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.

NVIDIA TrustZone Software contains a vulnerability in the Keymaster implementation where the software reads data past the end, or before the beginning, of the intended buffer; and may lead to denial of service or information disclosure. This issue is rated as high.

An out-of-bounds read in V8 in Google Chrome prior to 57.0.2987.133 for Linux, Windows, and Mac, and 57.0.2987.132 for Android, allowed a remote attacker to obtain heap memory contents via a crafted HTML page.

A use after free in printing in Google Chrome prior to 57.0.2987.133 for Linux and Windows allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page.

Transient DOS while processing an improperly formatted 802.11az Fine Time Measurement protocol frame.

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

Out of bound access in diag services when DCI command buffer reallocation is not done properly with required capacity in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables in APQ8009, APQ8096AU, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, QCS605, Rennell, SC8180X, SDM429W, SDM710, SDX55, SM7150, SM8150

In btm_ble_rand_enc_complete of btm_ble.cc, 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-13Android ID: A-260568354

Incorrect handling of complex species in V8 in Google Chrome prior to 57.0.2987.98 for Linux, Windows, and Mac and 57.0.2987.108 for Android allowed a remote attacker to execute arbitrary code via a crafted HTML page.

In ParseBqrLinkQualityEvt of btif_bqr.cc, 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-13Android ID: A-242993878

Possible buffer over read due to improper validation of SIB type when processing a NR system Information message in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile

Potential out of Bounds read in FIPS event processing due to improper validation of the length from the firmware in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile

Possible buffer over read due to improper calculation of string length while parsing Id3 tag in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables

Possible buffer overflow due to lack of buffer length check during management frame Rx handling in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile

Possible out of bound read due to improper validation of certificate chain in SSL or Internet key exchange in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables