Improper input validation vulnerability in parser_infe and sheifd_find_itemIndexin fuctions of libsimba library prior to SMR Apr-2022 Release 1 allows out of bounds write by privileged attackers.

Improper input validation vulnerability in parser_iloc and sheifd_find_itemIndexin fuctions of libsimba library prior to SMR Apr-2022 Release 1 allows out of bounds write by privileged attacker.

In OutputBuffersArray::realloc of CCodecBuffers.cpp, there is a possible heap disclosure due to a race condition. This could lead to remote information disclosure with System execution privileges needed. User interaction is needed for exploitation.Product: AndroidVersions: Android-10Android ID: A-142544793

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.

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.

Out of bounds read in WebGL in Google Chrome on Android prior to 148.0.7778.216 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

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.

Out of bounds read in ANGLE in Google Chrome prior to 111.0.5563.110 allowed a remote attacker who had compromised the renderer process to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

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

Out of bounds read in WebGL in Google Chrome on Android prior to 148.0.7778.216 allowed a remote attacker to leak cross-origin data via a crafted HTML page. (Chromium security severity: High)

In mca_ccb_hdl_rsp of mca_cact.cc in Android-7.0, Android-7.1.1, Android-7.1.2, Android-8.0, Android-8.1 and Android-9, 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. Android ID: A-116319076.

In rfc_process_mx_message of rfc_ts_frames.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: Android-7.0 Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android-9.0 Android ID: A-80432928

In hidh_l2cif_data_ind of hidh_conn.cc, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure over bluetooth with no additional execution privileges needed. User interaction is not needed for exploitation.

In smp_data_received of smp_l2c.cc, there is a possible out of bounds read followed by code execution due to a missing bounds check. This could lead to remote code execution with no additional execution privileges needed. User interaction is needed for exploitation.

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: Android-6.0 Android-6.0.1 Android-7.0 Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android ID: A-79164722.

Out of bounds read in GPU in Google Chrome on Mac prior to 148.0.7778.179 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: High)

In ih264e_fmt_conv_420p_to_420sp of ih264e_fmt_conv.c there is a possible out of bound read due to missing bounds check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is needed for exploitation.

In llcp_util_parse_cc of llcp_util.cc, there is a possible out-of-bound read due to a missing bounds check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is needed for exploitation.Product: AndroidVersions: Android-7.0 Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android-9Android ID: A-114237888

In gatt_process_error_rsp of gatt_cl.cc, there is a possible out of bound 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 sdpu_extract_attr_seq of sdp_utils.cc, there is a possible out of bounds read due to an incorrect bounds check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android Versions: Android-6.0 Android-6.0.1 Android-7.0 Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android ID: A-78136677.

In ihevcd_parse_slice_data of ihevcd_parse_slice.c there is a possible heap buffer out of bound read due to a missing bounds check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is needed for exploitation.

Out of bounds read in WebRTC in Google Chrome prior to 110.0.5481.77 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: High)

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. 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.

Out of bounds read in GPU in Google Chrome on prior to 148.0.7778.179 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: Medium)

Out of bounds read in Swiftshader in Google Chrome prior to 123.0.6312.58 allowed a remote attacker to perform out of bounds memory access via a crafted HTML page. (Chromium security severity: Medium)

MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle IEEE 1905 protocols. (Affected Chipsets MT7603E, MT7613, MT7615, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 2.0.2; Out-of-bounds read).

MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle IEEE 1905 protocols. (Affected Chipsets MT7603E, MT7613, MT7615, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 2.0.2; Out-of-bounds read).

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 GPU in Google Chrome on Linux prior to 148.0.7778.168 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. (Chromium security severity: Medium)

Determined not a vulnerability

MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle IEEE 1905 protocols. (Affected Chipsets MT7603E, MT7613, MT7615, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 2.0.2; Out-of-bounds read).

In wlan_hdd_cfg80211_set_privacy_ibss() 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, a buffer over-read can potentially occur.

Out of bounds read in ANGLE in Google Chrome prior to 148.0.7778.216 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. (Chromium security severity: High)

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.

Out of bounds read in GPU in Google Chrome prior to 148.0.7778.216 allowed a remote attacker who had compromised the renderer process 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. 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.

Out of bounds read in ANGLE in Google Chrome on Windows prior to 148.0.7778.216 allowed a remote attacker to execute arbitrary code via a crafted HTML page. (Chromium security severity: High)

MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle IEEE 1905 protocols. (Affected Chipsets MT7603E, MT7613, MT7615, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 2.0.2; Out-of-bounds read).

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In MP3 encoder, 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 WebAudio in Google Chrome prior to 95.0.4638.54 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

Out of bounds memory access in ANGLE in Google Chrome prior to 148.0.7778.216 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

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.

MediaTek microchips, as used in NETGEAR devices through 2021-11-11 and other devices, mishandle the WPS (Wi-Fi Protected Setup) protocol. (Affected Chipsets MT7603E, MT7610, MT7612, MT7613, MT7615, MT7620, MT7622, MT7628, MT7629, MT7915; Affected Software Versions 7.4.0.0; Out-of-bounds read).