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 execute arbitrary code inside a sandbox via a crafted HTML page, related to Array.prototype.indexOf.

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 Bluetooth, 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. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12LAndroid ID: A-205989472

In ProtocolStkProactiveCommandAdapter::Init of protocolstkadapter.cpp, 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-204585345References: N/A

In HandleTransactionIoEvent of actuator_driver.cc, there is a possible out of bounds read due to a heap buffer overflow. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-204421047References: N/A

In TuningProviderBase::GetTuningTreeSet of tuning_provider_base.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 kernelAndroid ID: A-205753190References: N/A

In btu_hcif_pin_code_request_evt, btu_hcif_link_key_request_evt, and btu_hcif_link_key_notification_evt of btu_hcif.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-142638492

In bpf_prog_test_run_skb of test_run.c, there is a possible out of bounds read due to Incorrect Size Value. 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-154175781References: Upstream kernel

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 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 TBD of TBD, there is a possible out of bounds read due to TBD. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-206039140References: N/A

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 ffu_flash_pack of ffu.c, there is a possible out of bounds read due to an integer overflow. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation.

Out of bounds memory access in V8 in Google Chrome prior to 144.0.7559.59 allowed a remote attacker to potentially exploit object corruption via a crafted HTML page. (Chromium security severity: High)

In startWpsPbcInternal of sta_iface.cpp, there is a possible out of bounds read due to improper input validation. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-262246082

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.

In multiple locations of p2p_iface.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-13Android ID: A-262236313

In MessageQueueBase of MessageQueueBase.h, there is a possible out of bounds read 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.Product: AndroidVersions: Android-13Android ID: A-247092734

In EUTRAN_LCS_DecodeFacilityInformationElement of LPP_LcsManagement.c, 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 kernelAndroid ID: A-247564044References: N/A

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: ALPS07573552; Issue ID: ALPS07573552.

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.

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.

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 ANGLE allowed a remote attacker to obtain sensitive data via a crafted HTML page.

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.

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.

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 read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples. We have patched the issue in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6. 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 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 end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. 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 if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. 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.

An issue was discovered in Adobe Flash Player 27.0.0.183 and earlier versions. This vulnerability occurs as a result of a computation that reads data that is past the end of the target buffer; the computation is part of providing language- and region- or country- specific functionality. The use of an invalid (out-of-range) pointer offset during access of internal data structure fields causes the vulnerability. A successful attack can lead to sensitive data exposure.

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 crus_afe_get_param of msm-cirrus-playback.c, there is a possible out of bounds read due to an integer overflow. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation.Product: Android. Versions: Android kernel. Android ID: A-139354541

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.

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.

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.

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 open source platform for machine learning. Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution. The fix will be included in TensorFlow version 2.12.0 and will also cherrypick this commit on TensorFlow version 2.11.1.

In heap of spaces.h, there is a possible out of bounds read due to improper input validation. This could lead to remote information disclosure when processing a proxy auto config file with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android Versions: Android-7.0 Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android-9 Android ID: A-117555811

TensorFlow is an open source platform for machine learning. Prior to versions 2.12.0 and 2.11.1, an out of bounds read is in GRUBlockCellGrad. A fix is included in TensorFlow 2.12.0 and 2.11.1.

An issue was discovered in Adobe Flash Player 27.0.0.183 and earlier versions. This vulnerability occurs as a result of a computation that reads data that is past the end of the target buffer; the computation is part of AdobePSDK metadata. The use of an invalid (out-of-range) pointer offset during access of internal data structure fields causes the vulnerability. A successful attack can lead to sensitive data exposure.

In wlan service, there is a possible out of bounds read due to improper input validation. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07588360; Issue ID: ALPS07588360.

NVIDIA Tegra kernel driver contains a vulnerability in NVIDIA NVDEC, where a user with high privileges might be able to read from or write to a memory location that is outside the intended boundary of the buffer, which may lead to denial of service, Information disclosure, loss of Integrity, or possible escalation of privileges.

Adobe Flash Player versions 25.0.0.127 and earlier have an exploitable memory corruption vulnerability in the ActionScript2 code parser. Successful exploitation could lead to arbitrary code execution.

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.

A vulnerability was discovered in the Zoom Client for Meetings (for Android, iOS, Linux, macOS, and Windows) before version 5.8.4, Zoom Client for Meetings for Blackberry (for Android and iOS) before version 5.8.1, Zoom Client for Meetings for intune (for Android and iOS) before version 5.8.4, Zoom Client for Meetings for Chrome OS before version 5.0.1, Zoom Rooms for Conference Room (for Android, AndroidBali, macOS, and Windows) before version 5.8.3, Controllers for Zoom Rooms (for Android, iOS, and Windows) before version 5.8.3, Zoom VDI Windows Meeting Client before version 5.8.4, Zoom VDI Azure Virtual Desktop Plugins (for Windows x86 or x64, IGEL x64, Ubuntu x64, HP ThinPro OS x64) before version 5.8.4.21112, Zoom VDI Citrix Plugins (for Windows x86 or x64, Mac Universal Installer & Uninstaller, IGEL x64, eLux RP6 x64, HP ThinPro OS x64, Ubuntu x64, CentOS x 64, Dell ThinOS) before version 5.8.4.21112, Zoom VDI VMware Plugins (for Windows x86 or x64, Mac Universal Installer & Uninstaller, IGEL x64, eLux RP6 x64, HP ThinPro OS x64, Ubuntu x64, CentOS x 64, Dell ThinOS) before version 5.8.4.21112, Zoom Meeting SDK for Android before version 5.7.6.1922, Zoom Meeting SDK for iOS before version 5.7.6.1082, Zoom Meeting SDK for macOS before version 5.7.6.1340, Zoom Meeting SDK for Windows before version 5.7.6.1081, Zoom Video SDK (for Android, iOS, macOS, and Windows) before version 1.1.2, Zoom on-premise Meeting Connector before version 4.8.12.20211115, Zoom on-premise Meeting Connector MMR before version 4.8.12.20211115, Zoom on-premise Recording Connector before version 5.1.0.65.20211116, Zoom on-premise Virtual Room Connector before version 4.4.7266.20211117, Zoom on-premise Virtual Room Connector Load Balancer before version 2.5.5692.20211117, Zoom Hybrid Zproxy before version 1.0.1058.20211116, and Zoom Hybrid MMR before version 4.6.20211116.131_x86-64 which potentially allowed for the exposure of the state of process memory. This issue could be used to potentially gain insight into arbitrary areas of the product's memory.

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

In on_iso_link_quality_read of btm_iso_impl.h, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure in the Bluetooth server with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-260568750

In btm_ble_batchscan_filter_track_adv_vse_cback of btm_ble_batchscan.cc, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure in the Bluetooth server with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-261857395