Improper Check for Unusual or Exceptional Conditions

OpenVPN Access Server 2.7.3 to 2.8.7 allows remote attackers to trigger an assert during the user authentication phase via incorrect authentication token data in an early phase of the user authentication resulting in a denial of service.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

Missing check in UIDL request handler in com.vaadin:flow-server versions 1.0.0 through 1.0.5 (Vaadin 10.0.0 through 10.0.7, and 11.0.0 through 11.0.2) allows attacker to update element property values via crafted synchronization message.Show less

In Juniper Networks Junos OS Evolved, receipt of a stream of specific genuine Layer 2 frames may cause the Advanced Forwarding Toolkit (AFT) manager process (Evo-aftmand), responsible for handling Route, Class-of-Service (CoS), Firewall operations within the packet forwarding engine (PFE) to crash and restart, leading to a Denial of Service (DoS) condition. By continuously sending this specific stream of genuine Layer 2 frames, an attacker can repeatedly crash the PFE, causing a sustained Denial of Service (DoS). This issue affects Juniper Networks Junos OS Evolved: All versions prior to 20.4R1-EVO. This issue does not affect Junos OS versions.Show less

Due to an improper check for unusual or exceptional conditions in Juniper Networks Junos OS and Junos OS Evolved the Routing Protocol Daemon (RPD) service, upon receipt of a specific matching BGP packet meeting a specific term in the flowspec configuration, crashes and restarts causing a Denial of Service (DoS). Continued receipt and processing of this packet will create a sustained Denial of Service (DoS) condition. This issue affects only Multiprotocol BGP (MP-BGP) VPNv6 FlowSpec deployments. This issue affects: Juniper Networks Junos OS: 18.4 versions prior to 18.4R1-S8, 18.4R2-S7, 18.4R3-S7; 19.1 versions prior to 19.1R2-S2, 19.1R3-S4; 19.2 versions prior to 19.2R1-S6, 19.2R3-S2; 19.3 versions prior to 19.3R3-S2; 19.4 versions prior to 19.4R2-S4, 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 Evolved: All versions after 18.4R1-EVO prior to 20.3R2-EVO. This issue does not affect: Juniper Networks Junos OS versions prior to 18.4R1. Juniper Networks Junos OS Evolved versions prior to 18.4R1-EVO.Show less

An improper check for unusual or exceptional conditions vulnerability in Juniper Networks MX Series platforms with Trio-based MPC (Modular Port Concentrator) deployed in (Ethernet VPN) EVPN-(Virtual Extensible LAN) VXLAN configuration, may allow an attacker sending specific Layer 2 traffic to cause Distributed Denial of Service (DDoS) protection to trigger unexpectedly, resulting in traffic impact. Continued receipt and processing of this specific Layer 2 frames will sustain the Denial of Service (DoS) condition. An indication of compromise is to check DDOS LACP violations: user@device> show ddos-protection protocols statistics brief | match lacp This issue only affects the MX Series platforms with Trio-based MPC. No other products or platforms are affected. This issue affects: Juniper Networks Junos OS on MX Series: 15.1 versions prior to 15.1R7-S9; 17.3 versions prior to 17.3R3-S11; 17.4 versions prior to 17.4R3-S4; 18.1 versions prior to 18.1R3-S12; 18.2 versions prior to 18.2R2-S8, 18.2R3-S8; 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 19.1R3-S4; 19.2 versions prior to 19.2R1-S6; 19.3 versions prior to 19.3R3-S2; 19.4 versions prior to 19.4R2-S4, 19.4R3-S2; 20.1 versions prior to 20.1R2, 20.1R3; 20.2 versions prior to 20.2R2-S1, 20.2R3; 20.3 versions prior to 20.3R1-S1, 20.3R2;Show less

An Improper Check for Unusual or Exceptional Conditions in Juniper Networks Junos OS Evolved may cause the stateless firewall filter configuration which uses the action 'policer' in certain combinations with other options to not take effect. An administrator can use the following CLI command to see the failures with filter configuration: user@device> show log kfirewall-agent.log | match ERROR Jul 23 14:16:03 ERROR: filter not supported This issue affects Juniper Networks Junos OS Evolved: Versions 19.1R1-EVO and above prior to 20.3R1-S2-EVO, 20.3R2-EVO. This issue does not affect Juniper Networks Junos OS.Show less