| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Envoy is an open source L7 proxy and communication bus designed for large modern service oriented architectures. In affected versions Envoy transitions a H/2 connection to the CLOSED state when it receives a GOAWAY frame without any streams outstanding. The connection state is transitioned to DRAINING when it receives a SETTING frame with the SETTINGS_MAX_CONCURRENT_STREAMS parameter set to 0. Receiving these two frames in the same I/O event results in abnormal termination of the Envoy process due to invalid state transition from CLOSED to DRAINING. A sequence of H/2 frames delivered by an untrusted upstream server will result in Denial of Service in the presence of untrusted **upstream** servers. Envoy versions 1.19.1, 1.18.4 contain fixes to stop processing of pending H/2 frames after connection transition to the CLOSED state. |
| An issue was discovered in Ruby through 2.6.7, 2.7.x through 2.7.3, and 3.x through 3.0.1. Net::IMAP does not raise an exception when StartTLS fails with an an unknown response, which might allow man-in-the-middle attackers to bypass the TLS protections by leveraging a network position between the client and the registry to block the StartTLS command, aka a "StartTLS stripping attack." |
| Mintty before 3.4.7 mishandles Bracketed Paste Mode. |
| An Improper Check for Unusual or Exceptional Conditions vulnerability combined with a Race Condition in the flow daemon (flowd) of Juniper Networks Junos OS on SRX300 Series, SRX500 Series, SRX1500, and SRX5000 Series with SPC2 allows an unauthenticated network based attacker sending specific traffic to cause a crash of the flowd/srxpfe process, responsible for traffic forwarding in SRX, which will cause a Denial of Service (DoS). Continued receipt and processing of this specific traffic will create a sustained Denial of Service (DoS) condition. This issue can only occur when specific packets are trying to create the same session and logging for session-close is configured as a policy action. Affected platforms are: SRX300 Series, SRX500 Series, SRX1500, and SRX5000 Series with SPC2. Not affected platforms are: SRX4000 Series, SRX5000 Series with SPC3, and vSRX Series. This issue affects Juniper Networks Junos OS SRX300 Series, SRX500 Series, SRX1500, and SRX5000 Series with SPC2: All versions prior to 17.4R3-S5; 18.3 versions prior to 18.3R3-S5; 18.4 versions prior to 18.4R3-S9; 19.1 versions prior to 19.1R3-S6; 19.2 versions prior to 19.2R1-S7, 19.2R3-S2; 19.3 versions prior to 19.3R2-S6, 19.3R3-S2; 19.4 versions prior to 19.4R1-S4, 19.4R3-S3; 20.1 versions prior to 20.1R2-S2, 20.1R3; 20.2 versions prior to 20.2R3; 20.3 versions prior to 20.3R2-S1, 20.3R3; 20.4 versions prior to 20.4R2. |
| 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. |
| An Improper Handling of Exceptional Conditions vulnerability in Juniper Networks Junos OS and Junos OS Evolved allows an attacker to inject a specific BGP update, causing the routing protocol daemon (RPD) to crash and restart, leading to a Denial of Service (DoS). Continued receipt and processing of the BGP update will create a sustained Denial of Service (DoS) condition. This issue affects very specific versions of Juniper Networks Junos OS: 19.3R3-S2; 19.4R3-S3; 20.2 versions 20.2R2-S3 and later, prior to 20.2R3-S2; 20.3 versions 20.3R2 and later, prior to 20.3R3; 20.4 versions 20.4R2 and later, prior to 20.4R3; 21.1 versions prior to 21.1R2. Juniper Networks Junos OS 20.1 is not affected by this issue. This issue also affects Juniper Networks Junos OS Evolved: All versions prior to 20.4R2-S3-EVO, 20.4R3-EVO; 21.1-EVO versions prior to 21.1R2-EVO; 21.2-EVO versions prior to 21.2R2-EVO. |
| 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; |
| A vulnerability in Apache Tomcat allows an attacker to remotely trigger a denial of service. An error introduced as part of a change to improve error handling during non-blocking I/O meant that the error flag associated with the Request object was not reset between requests. This meant that once a non-blocking I/O error occurred, all future requests handled by that request object would fail. Users were able to trigger non-blocking I/O errors, e.g. by dropping a connection, thereby creating the possibility of triggering a DoS. Applications that do not use non-blocking I/O are not exposed to this vulnerability. This issue affects Apache Tomcat 10.0.3 to 10.0.4; 9.0.44; 8.5.64. |
| Possible denial of service due to improper handling of debug register trap from user applications in Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile |
| VIGRA Computer Vision Library Version-1-11-1 contains a segmentation fault vulnerability in the impex.hxx read_image_band() function, in which a crafted file can cause a denial of service. |
| TensorFlow is an end-to-end open source platform for machine learning. Passing invalid arguments (e.g., discovered via fuzzing) to `tf.raw_ops.SparseCountSparseOutput` results in segfault. 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. Passing a complex argument to `tf.transpose` at the same time as passing `conjugate=True` argument results in a crash. 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 denial of service via `CHECK`-fail in `tf.strings.substr` with invalid arguments. 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. 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 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. |
| 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. |
| 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. |
| 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 intel_pmu_drain_pebs_nhm in arch/x86/events/intel/ds.c in the Linux kernel through 5.11.8 on some Haswell CPUs, userspace applications (such as perf-fuzzer) can cause a system crash because the PEBS status in a PEBS record is mishandled, aka CID-d88d05a9e0b6. |
| In the standard library in Rust before 1.52.0, the Zip implementation has a panic safety issue. It calls __iterator_get_unchecked() more than once for the same index when the underlying iterator panics (in certain conditions). This bug could lead to a memory safety violation due to an unmet safety requirement for the TrustedRandomAccess trait. |
