Junos OS: QFX Series and PTX Series: FPC resource usage increases when certain packets are processed which are being VXLAN encapsulated

An Improper Check for Unusual or Exceptional Conditions vulnerability combined with Improper Handling of Exceptional Conditions in Juniper Networks Junos OS on QFX Series and PTX Series allows an unauthenticated network based attacker to cause increased FPC CPU utilization by sending specific IP packets which are being VXLAN encapsulated leading to a partial Denial of Service (DoS). Continued receipted of these specific traffic will create a sustained Denial of Service (DoS) condition. This issue affects: Juniper Networks Junos OS on QFX Series: All versions prior to 17.3R3-S11; 17.4 versions prior to 17.4R2-S13, 17.4R3-S4; 18.1 versions prior to 18.1R3-S12; 18.2 versions prior to 18.2R2-S8, 18.2R3-S7; 18.3 versions prior to 18.3R3-S4; 18.4 versions prior to 18.4R1-S8, 18.4R2-S7, 18.4R3-S7; 19.1 versions prior to 19.1R1-S6, 19.1R2-S2, 19.1R3-S4; 19.2 versions prior to 19.2R1-S6, 19.2R3-S2; 19.3 versions prior to 19.3R3-S1; 19.4 versions prior to 19.4R2-S3, 19.4R3-S1; 20.1 versions prior to 20.1R2, 20.1R3; 20.2 versions prior to 20.2R2, 20.2R3; 20.3 versions prior to 20.3R1-S1, 20.3R2. Juniper Networks Junos OS on PTX Series: All versions prior to 18.4R3-S9; 19.1 versions prior to 19.1R3-S6; 19.2 versions prior to 19.2R1-S7, 19.2R3-S3; 19.3 versions prior to 19.3R2-S6, 19.3R3-S3; 19.4 versions prior to 19.4R1-S4, 19.4R3-S5; 20.1 versions prior to 20.1R2-S2, 20.1R3; 20.2 versions prior to 20.2R3-S1; 20.3 versions prior to 20.3R2-S1, 20.3R3; 20.4 versions prior to 20.4R2-S1, 20.4R3; 21.1 versions prior to 21.1R1-S1, 21.1R2.

Junos OS: MX Series: Receipt of specific packet on MS-MPC/MS-MIC causes line card reset

An Improper Check for Unusual or Exceptional Conditions in packet processing on the MS-MPC/MS-MIC utilized by Juniper Networks Junos OS allows a malicious attacker to send a specific packet, triggering the MS-MPC/MS-MIC to reset, causing a Denial of Service (DoS). Continued receipt and processing of this packet will create a sustained Denial of Service (DoS) condition. This issue only affects specific versions of Juniper Networks Junos OS on MX Series: 17.3R3-S11; 17.4R2-S13; 17.4R3 prior to 17.4R3-S5; 18.1R3-S12; 18.2R2-S8, 18.2R3-S7, 18.2R3-S8; 18.3R3-S4; 18.4R3-S7; 19.1R3-S4, 19.1R3-S5; 19.2R1-S6; 19.3R3-S2; 19.4R2-S4, 19.4R2-S5; 19.4R3-S2; 20.1R2-S1; 20.2R2-S2, 20.2R2-S3, 20.2R3; 20.3R2, 20.3R2-S1; 20.4R1, 20.4R1-S1, 20.4R2; 21.1R1; This issue does not affect any version of Juniper Networks Junos OS prior to 15.1X49-D240;

Incomplete validation in `SparseSparseMinimum`

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.

CHECK-fail in `QuantizeAndDequantizeV4Grad`

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.

CHECK-fail in SparseConcat

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.SparseConcat`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in `shapes[0]` as dimensions for the output shape. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. 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.

CHECK-fail in DrawBoundingBoxes

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. 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.

CHECK-fail in tf.raw_ops.EncodePng

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.

An issue was discovered in JerryScript 2.4.0. There is a SEGV in main_print_unhandled_exception in main-utils.c file.

Improper check or handling of exception conditions vulnerability in Samsung Pay (US only) prior to version 4.0.65 allows attacker to use NFC without user recognition.

An improper error handling in Exynos CP booting driver prior to SMR Oct-2021 Release 1 allows local attackers to bypass a Secure Memory Protector of Exynos CP Memory.

Improper check vulnerability in Samsung Health prior to version 6.17 allows attacker to read internal cache data via exported component.

Denial of Service (DoS)

All versions of package mongo-express are vulnerable to Denial of Service (DoS) when exporting an empty collection as CSV, due to an unhandled exception, leading to a crash.

A CWE-754: Improper Check for Unusual or Exceptional Conditions vulnerability exists that could cause a Denial of Service of the RTU when receiving a specially crafted request over Modbus, and the RTU is configured as a Modbus server. Affected Products: SCADAPack 312E, 313E, 314E, 330E, 333E, 334E, 337E, 350E and 357E RTUs with firmware V8.18.1 and prior

Improper Check for Unusual or Exceptional Conditions vulnerability exists in Triconex Model 3009 MP installed on Tricon V11.3.x systems that could cause module reset when TCM receives malformed TriStation packets while the write-protect keyswitch is in the program position. This CVE ID is unique from CVE-2021-22742, CVE-2021-22744, CVE-2021-22745, and CVE-2021-22746.

Improper Check for Unusual or Exceptional Conditions vulnerability exists in Triconex Model 3009 MP installed on Tricon V11.3.x systems that could cause module reset when TCM receives malformed TriStation packets while the write-protect keyswitch is in the program position. This CVE ID is unique from CVE-2021-22742, CVE-2021-22744, CVE-2021-22745, and CVE-2021-22747.

Improper Check for Unusual or Exceptional Conditions vulnerability exists in Triconex Model 3009 MP installed on Tricon V11.3.x systems that could cause module reset when TCM receives malformed TriStation packets while the write-protect keyswitch is in the program position. This CVE ID is unique from CVE-2021-22742, CVE-2021-22744, CVE-2021-22746, and CVE-2021-22747.

Improper Check for Unusual or Exceptional Conditions vulnerability exists in Triconex Model 3009 MP installed on Tricon V11.3.x systems that could cause module reset when TCM receives malformed TriStation packets while the write-protect keyswitch is in the program position. This CVE ID is unique from CVE-2021-22742, CVE-2021-22745, CVE-2021-22746, and CVE-2021-22747.

Improper Check for Unusual or Exceptional Conditions vulnerability exists in Triconex TCM 4351B installed on Tricon V11.3.x systems that could cause module reset when TCM receives malformed TriStation packets while the write-protect keyswitch is in the program position.

Improper Check for Unusual or Exceptional Conditions vulnerability exists in Triconex Model 3009 MP installed on Tricon V11.3.x systems that could cause module reset when TCM receives malformed TriStation packets while the write-protect keyswitch is in the program position.

There is an Improper Check for Unusual or Exceptional Conditions Vulnerability in Huawei Smartphone.Successful exploitation of this vulnerability may cause the system to reset.