arch/x86/kernel/entry_64.S in the Linux kernel before 3.17.5 does not properly handle faults associated with the Stack Segment (SS) segment register, which allows local users to gain privileges by triggering an IRET instruction that leads to access to a GS Base address from the wrong space.

In android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, a double free can occur when kmalloc fails to allocate memory for pointers resp/req in the service-locator driver function service_locator_send_msg().

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. 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. Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array(https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion. 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.

In Settings, there is a possible bypass of profile owner restrictions due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In System UI, there is a possible factory reset protection bypass due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In Settings, there is a possible restriction bypass due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

Race condition in the ip4_datagram_release_cb function in net/ipv4/datagram.c in the Linux kernel before 3.15.2 allows local users to gain privileges or cause a denial of service (use-after-free) by leveraging incorrect expectations about locking during multithreaded access to internal data structures for IPv4 UDP sockets.

An issue was discovered on Samsung mobile devices with N(7.x), O(8.x), and P(9.0) software. There is local SQL injection in the Gear VR Service Content Provider. The Samsung ID is SVE-2019-14058 (July 2019).

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, while doing sha and cipher operations, a userspace buffer is directly accessed in kernel space potentially leading to a page fault.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, in the coresight-tmc driver, a simultaneous read and enable of the ETR device after changing the buffer size may result in a Use After Free condition of the previous buffer.

Inappropriate implementation in the ChromeOS Readiness Tool installer on Windows prior to 1.0.2.0 loosens DCOM access rights on two objects allowing an attacker to potentially bypass discretionary access controls.

An issue was discovered on Samsung mobile devices with N(7.x), O(8.x), and P(9.0) software. There is local SQL injection in the RCS Content Provider. The Samsung IDs are SVE-2019-14059, SVE-2019-14685 (August 2019).

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).

TensorFlow is an end-to-end open source platform for machine learning. The fix for CVE-2020-15209(https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209) missed the case when the target shape of `Reshape` operator is given by the elements of a 1-D tensor. As such, the fix for the vulnerability(https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074) allowed passing a null-buffer-backed tensor with a 1D shape. 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.

Unquoted Windows search path vulnerability in the GoogleChromeDistribution::DoPostUninstallOperations function in installer/util/google_chrome_distribution.cc in the uninstall-survey feature in Google Chrome before 40.0.2214.91 allows local users to gain privileges via a Trojan horse program in the %SYSTEMDRIVE% directory, as demonstrated by program.exe, a different vulnerability than CVE-2015-1205.

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, if cmd_pkt and reg_pkt are called from different userspace threads, a use after free condition can potentially occur in wdsp_glink_write().

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, in compatibility mode, flash_data from 64-bit userspace may cause disclosure of kernel memory or a fault due to using a userspace-provided address.

An issue was discovered on Samsung mobile devices with O(8.x), P(9.0), and Q(10.0) software. Attackers can bypass Factory Reset Protection (FRP) via a SIM card. The Samsung ID is SVE-2019-16193 (February 2020).

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.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, while calculating CRC for GPT header fields with partition entries greater than 16384 buffer overflow occurs.

There is a possible LCS signing enforcement missing due to test/debugging code left in a production build. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

An issue was discovered on Samsung mobile devices with P(9.0) (Exynos chipsets) software. The Wi-Fi kernel drivers have a stack overflow. The Samsung IDs are SVE-2019-14965, SVE-2019-14966, SVE-2019-14968, SVE-2019-14969, SVE-2019-14970, SVE-2019-14980, SVE-2019-14981, SVE-2019-14982, SVE-2019-14983, SVE-2019-14984, SVE-2019-15122, SVE-2019-15123 (November 2019).

An issue was discovered on Samsung mobile devices with N(7.x), O(8.x), and P(9.0) software. There is local SQL injection in the Wi-Fi history Content Provider. The Samsung ID is SVE-2019-14061 (August 2019).

In the seccomp implementation prior to kernel version 4.8, there is a possible seccomp bypass due to seccomp policies that allow the use of ptrace. 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-119769499

An elevation of privilege vulnerability in the Upstream kernel video driver. Product: Android. Versions: Android kernel. Android ID: A-37950620.

In ppmp_unprotect_buf of drm/code/drm_fw.c, there is a possible memory corruption due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In SetScanResponseData of ble_advertiser_hci_interface.cc, there is a possible out-of-bound write due to a missing bounds check. 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-8.0 Android-8.1 Android-9. Android ID: A-121145627.

An elevation of privilege vulnerability in the MediaTek soc driver. Product: Android. Versions: Android kernel. Android ID: A-65025090. References: M-ALPS02973195.

An elevation of privilege vulnerability in the NVIDIA firmware processing code. Product: Android. Versions: Android kernel. Android ID: A-34112726. References: N-CVE-2017-0744.

Use-after-free vulnerability in the snd_pcm_info function in the ALSA subsystem in the Linux kernel allows attackers to gain privileges via unspecified vectors.

In multiple locations, there is a possible way to obtain any system permission due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is needed for exploitation.

In updateInternal of MediaProvider.java , there is a possible access of another app's files due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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.

In onReceive of AppRestrictionsFragment.java, there is a possible escalation of privilege due to unsafe deserialization. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is needed for exploitation.

In onPrimaryClipChanged of ClipboardListener.java, there is a possible way to partially bypass lock screen. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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.

In handleMessage of UsbDeviceManager.java, there is a possible method to access device contents over USB without unlocking the device due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In Settings, there is a possible way for the user to change SIM due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

On Samsung mobile devices with O(8.0) and P(9.0) software and an Exynos 8895 chipset, RKP (aka the Samsung Hypervisor EL2 implementation) allows arbitrary memory write operations. The Samsung ID is SVE-2019-16265.

In multiple locations, there is a possible way to obtain access to a folder due to a tapjacking/overlay attack. This could lead to local escalation of privilege with User execution privileges needed. User interaction is needed for exploitation.

In installExistingPackageAsUser of InstallPackageHelper.java, there is a possible carrier restriction bypass due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In Sim, there is a possible way to evade mobile preference restrictions due to a permission bypass. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In libaudioclient, there is a possible out of bounds write due to a use after free. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In Package Installer, there is a possible way to determine whether an app is installed, without query permissions, due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In ActivityStarter, there is a possible background activity launch due to an unsafe PendingIntent. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

In Permission Manager, there is a possible way to bypass required permissions due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

Uncaught exception for some Intel Unison software may allow an authenticated user to potentially enable escalation of privilege via local access.