In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In transportDec_OutOfBandConfig of tpdec_lib.cpp, there is a possible out of bounds write due to a heap buffer overflow. This could lead to remote code execution 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-224314979

In wlan, there is a possible denial of service due to incorrect error handling. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS08861558; Issue ID: MSV-1526.

In pq, there is a possible write-what-where condition due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS08495937; Issue ID: ALPS08495937.

Uncaught exception for some Intel Unison software may allow an authenticated user to potentially enable denial of service via network access.

In ShortcutInfo of ShortcutInfo.java, there is a possible way for an app to retain notification listening access due to an uncaught exception. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In onAccessPointChanged of AccessPointPreference.java, there is a possible way for unprivileged apps to receive a broadcast about WiFi access point change and its BSSID or SSID due to a precondition check failure. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.

In __efi_rt_asm_wrapper of efi-rt-wrapper.S, there is a possible bypass of shadow stack protection 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.Product: AndroidVersions: Android kernelAndroid ID: A-260821414References: Upstream kernel

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`. This is because the implementation does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`. However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.

An issue was discovered on Samsung mobile devices with M(6.0) and N(7.x) software. There is an arbitrary write in a trustlet. The Samsung ID is SVE-2017-8893 (August 2017).

An issue was discovered on Samsung mobile devices with N(7.x) software. There is a WifiStateMachine IllegalArgumentException and reboot if a malformed wpa_supplicant.conf is read. The Samsung ID is SVE-2017-9828 (October 2017).

In network HW, there is a possible system hang due to an uncaught exception. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: WCNCR00399035; Issue ID: MSV-2380.

Failure to check error conditions in V8 in Google Chrome prior to 72.0.3626.81 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. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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 several methods of JobStore.java, uncaught exceptions in job map parsing could lead to local persistent denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-246541702

An issue was discovered in OpenWrt 18.06.0 to 18.06.6 and 19.07.0, and LEDE 17.01.0 to 17.01.7. A bug in the fork of the opkg package manager before 2020-01-25 prevents correct parsing of embedded checksums in the signed repository index, allowing a man-in-the-middle attacker to inject arbitrary package payloads (which are installed without verification).

In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.

An issue was discovered on Samsung mobile devices with Q(10.0) (Galaxy S20) software. Because HAL improperly checks versions, bootloading by the S.LSI NFC chipset is mishandled. The Samsung ID is SVE-2020-16169 (August 2020).

In Modem, there is a possible system crash due to incorrect error handling. This could lead to remote denial of service, if a UE has connected to a rogue base station controlled by the attacker, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: MOLY01311265; Issue ID: MSV-4655.

Improper conditions check in multiple Intel® Processors may allow an authenticated user to potentially enable partial escalation of privilege, denial of service and/or information disclosure via local access.