While processing the system path, an out of bounds access can occur 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.

While processing the USB StrSerialDescriptor array, an array index out of bounds can occur 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 occur during a fast initial link setup (FILS) connection 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.

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 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.

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 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.

Out-of-Bounds access in TZ due to invalid index calculated to check against DDR in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in IPQ8074, MDM9206, MDM9607, MDM9650, MDM9655, MSM8996AU, QCA8081, Qualcomm 215, SD 410/12, SD 425, SD 427, SD 430, SD 435, SD 439 / SD 429, SD 450, SD 625, SD 632, SD 650/52, SD 820, SD 820A, SDM439, Snapdragon_High_Med_2016

In all android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, Buffer overread may occur due to non-null terminated strings while processing vsprintf in camera jpeg driver.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while processing diag event after associating to a network out of bounds read occurs if ssid of the network joined is greater than max limit.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while processing start bss request from upper layer, out of bounds read occurs if ssid length is greater than maximum.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, a buffer over-read can occur In the WMA NDP event handler functions due to lack of validation of input value event_info which is received from FW.

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).

In kisd, 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: Android; Versions: Android-11; Patch ID: ALPS05449968.

NVIDIA SHIELD TV, all versions prior to 8.2.2, contains a vulnerability in the NVDEC component, in which an attacker can read from or write to a memory location that is outside the intended boundary of the buffer, which may lead to denial of service or escalation of privileges.

In nci_proc_rf_management_ntf of nci_hrcv.cc, 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-11Android ID: A-164440989

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.

In ccci, 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: ALPS06108658; Issue ID: ALPS06108658.

In phNxpNciHal_send_ext_cmd of phNxpNciHal_ext.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege in the NFC server with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11Android ID: A-153731369

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 skb_headlen of /include/linux/skbuff.h, there is a possible out of bounds read due to memory corruption. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-153881554

In getAppSize of InstalldNativeService.cpp, 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-12LAndroid ID: A-220733817

While reading file class type from ELF header, a buffer overread may happen if the ELF file size is less than the size of ELF64 header size in Small Cell SoC, Snapdragon Automobile, Snapdragon Mobile, Snapdragon Wear in version FSM9055, MDM9206, MDM9607, MDM9650, MSM8909W, MSM8996AU, SD 210/SD 212/SD 205, SD 425, SD 430, SD 450, SD 615/16/SD 415, SD 625, SD 650/52, SD 820, SD 820A, SD 835, SD 845, SDA660, SDX20.

Out of bound read can happen due to lack of NULL termination on user controlled data in WLAN in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music in MSM8996AU, QCS405, QCS605, SD 210/SD 212/SD 205, SD 425, SD 427, SD 430, SD 435, SD 450, SD 625, SD 636, SD 665, SD 675, SD 712 / SD 710 / SD 670, SD 730, SD 820A, SD 835, SD 845 / SD 850, SD 855, SDA660, SDM630, SDM660, SDX24

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, improper input validation for vdev_id in wma_unified_bcntx_status_event_handler() which is received from firmware leads to potential out of bounds memory read.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, improper input validation for vdev_map in wma_tbttoffset_update_event_handler(), which is received from firmware, leads to potential buffer overwrite and out of bounds memory read.

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, while processing a StrHwPlatform with length smaller than EFICHIPINFO_MAX_ID_LENGTH, an array out of bounds access may occur.

Improper casting of structure while handling the buffer leads to out of bound read in display in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in MDM9150, MDM9206, MDM9607, MDM9650, MSM8909W, MSM8996AU, QCS405, QCS605, Qualcomm 215, SD 210/SD 212/SD 205, SD 425, SD 427, SD 430, SD 435, SD 439 / SD 429, SD 450, SD 615/16/SD 415, SD 625, SD 632, SD 636, SD 665, SD 675, SD 712 / SD 710 / SD 670, SD 730, SD 820, SD 820A, SD 835, SD 845 / SD 850, SD 855, SDA660, SDM439, SDM630, SDM660, SDX20

Possibility of out-of-bound read if id received from SPI is not in range of FIFO in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in IPQ4019, IPQ8064, MSM8909W, MSM8996AU, QCA9980, QCS605, Qualcomm 215, SD 425, SD 439 / SD 429, SD 450, SD 625, SD 632, SD 636, SD 712 / SD 710 / SD 670, SD 820A, SD 845 / SD 850, SD 855, SDM439, SDM660, SDX24

In all android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while processing the boot image header, an out of bounds read can occur in boot.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB read on heap in the TFLite implementation of `Split_V`(https://github.com/tensorflow/tensorflow/blob/c59c37e7b2d563967da813fa50fe20b21f4da683/tensorflow/lite/kernels/split_v.cc#L99). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the `SizeOfDimension` function(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/kernel_util.h#L148-L150) will access data outside the bounds of the tensor shape array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Possible buffer overflow and over read possible due to missing bounds checks for fixed limits if we consider widevine HLOS client as non-trustable in Snapdragon Auto, Snapdragon Compute, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking in Kamorta, QCS404, Rennell, SC7180, SDX55, SM6150, SM7150, SM8250, SXR2130

Possible out of bound memory access while playing a crafted clip in media player in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in SM8150

Possibility of out of bound access in debug queue, if packet size field is corrupted in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8009, APQ8017, APQ8053, APQ8096AU, APQ8098, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8909W, MSM8917, MSM8920, MSM8937, MSM8940, MSM8953, MSM8996AU, MSM8998, Nicobar, QCN7605, QCS405, QCS605, QM215, SA6155P, SDA660, SDA845, SDM429, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM670, SDM710, SDM845, SDX20, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130

Buffer over-read can occur in fast message handler due to improper input validation while processing a message from firmware in Snapdragon Auto, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music in APQ8053, APQ8096AU, MSM8996AU, MSM8998, QCN7605, QCS405, QCS605, SDA660, SDM636, SDM660, SDX20, SDX24

Lack of check of extscan change results received from firmware can lead to an out of buffer read in Snapdragon Auto, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music in MDM9150, MDM9206, MDM9607, MDM9640, MDM9650, MSM8996AU, QCA6174A, QCA6574AU, QCA9377, QCA9379, QCS605, SD 210/SD 212/SD 205, SD 425, SD 430, SD 600, SD 625, SD 636, SD 665, SD 675, SD 712 / SD 710 / SD 670, SD 730, SD 820A, SD 835, SD 845 / SD 850, SD 855, SDA660, SDM630, SDM660, SDX20, SDX24

Possible OOB issue in EEPROM due to lack of check while accessing memory map array at the time of reading operation in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8009, APQ8053, MSM8909W, MSM8917, MSM8953, Nicobar, QCS405, QCS605, QM215, SA6155P, SDA845, SDM429, SDM439, SDM450, SDM632, SDM670, SDM710, SDM845, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130

Insufficient memory allocation in boot due to incorrect size being passed could result in out of bounds access in Small Cell SoC, Snapdragon Automobile, Snapdragon Mobile and Snapdragon Wear in version FSM9055, MDM9206, MDM9607, MDM9640, MDM9650, MSM8909W, MSM8996AU, SD 210/SD 212/SD 205, SD 425, SD 430, SD 450, SD 615/16/SD 415, SD 617, SD 625, SD 650/52, SD 810, SD 820, SD 820A, SD 835, SDA660 and SDX20

Buffer over-read vulnerabilities in an older version of ASN.1 parser in Snapdragon Mobile in versions SD 600.

In android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, possible buffer overflow or information leak in the functions "sme_set_ft_ies" and "csr_roam_issue_ft_preauth_req" due to incorrect initialization of WEXT callbacks and lack of the checks for buffer size.

In the serialization functions of StatsLogEventWrapper.java, there is a possible out-of-bounds write due to unnecessary functionality which may be abused. This could lead to local escalation of privilege in the system process with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android-9. Android ID: A-112550251

In sdcardfs_create and sdcardfs_mkdir of inode.c, there is a possible memory corruption due to improper locking. 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 kernel Android ID: A-111641492 References: N/A

In Parcel.writeMapInternal of Parcel.java, there is a possible parcel serialization/deserialization mismatch due to improper input validation. 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-7.0 Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android-9. Android ID: A-112859604

In get_futex_key of futex.c, there is a use-after-free due to improper locking. This could lead to local escalation of privilege with no additional privileges needed. User interaction is not needed for exploitation. Product: Android Versions: Android kernel Android ID: A-74250718 References: Upstream kernel.

In really_install_package of install.cpp, there is a possible free of arbitrary memory due to uninitialized data. 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-7.0 Android-7.1.1 Android-7.1.2. Android ID: A-35385357.