| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| All versions of package dojo are vulnerable to Prototype Pollution via the setObject function. |
| This affects all versions of package github.com/nats-io/nats-server/server. Untrusted accounts are able to crash the server using configs that represent a service export/import cycles. Disclaimer from the maintainers: Running a NATS service which is exposed to untrusted users presents a heightened risk. Any remote execution flaw or equivalent seriousness, or denial-of-service by unauthenticated users, will lead to prompt releases by the NATS maintainers. Fixes for denial of service issues with no threat of remote execution, when limited to account holders, are likely to just be committed to the main development branch with no special attention. Those who are running such services are encouraged to build regularly from git. |
| NATS Server 2.x before 2.2.0 and JWT library before 2.0.1 have Incorrect Access Control because Import Token bindings are mishandled. |
| An integer overflow in NATS Server before 2.0.2 allows a remote attacker to crash the server by sending a crafted request. If authentication is enabled, then the remote attacker must have first authenticated. |
| Everest, later referred to as AIDA64, 5.50.2100 contains a denial of service vulnerability that allows local attackers to crash the application by manipulating file open functionality. Attackers can generate a 450-byte buffer of repeated characters and paste it into the file open dialog to trigger an application crash. |
| Tekton Pipelines project provides k8s-style resources for declaring CI/CD-style pipelines. Versions 0.60.0 through 1.0.0, 1.1.0 through 1.3.2, 1.4.0 through 1.6.0, 1.7.0 through 1.9.0, 1.10.0, and 1.10.1 have a denial-of-service vulnerability in that allows any user who can create a TaskRun or PipelineRun to crash the controller cluster-wide by setting .spec.taskRef.resolver (or .spec.pipelineRef.resolver) to a string of 31+ characters. The crash occurs because GenerateDeterministicNameFromSpec produces a name exceeding the 63-character DNS-1123 label limit, and its truncation logic panics on a [-1] slice bound since the generated name contains no spaces. Once crashed, the controller enters a CrashLoopBackOff on restart (as it re-reconciles the offending resource), blocking all CI/CD reconciliation until the resource is manually deleted. Built-in resolvers (git, cluster, bundles, hub) are unaffected due to their short names, but any custom resolver name triggers the bug. The fix truncates the resolver-name prefix instead of the full string, preserving the hash suffix for determinism and uniqueness. This issue has been patched in versions 1.0.1, 1.3.3, 1.6.1, 1.9.2 and 1.10.2. |
| Strimzi provides a way to run an Apache Kafka cluster on Kubernetes or OpenShift in various deployment configurations. From 0.47.0 and prior to 0.49.1, in some situations, Strimzi creates an incorrect Kubernetes Role which grants the Apache Kafka Connect and Apache Kafka MirrorMaker 2 operands the GET access to all Kubernetes Secrets that exist in the given Kubernetes namespace. The issue is fixed in Strimzi 0.49.1. |
| An issue was discovered in PyTorch v2.5 and v2.7.1. Omission of profiler.stop() can cause torch.profiler.profile (PythonTracer) to crash or hang during finalization, leading to a Denial of Service (DoS). |
| An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor. |
| pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together. |
| A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS). |
| pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long(). |
| A Name Error occurs in pytorch v2.7.0 when a PyTorch model consists of torch.cummin and is compiled by Inductor, leading to a Denial of Service (DoS). |
| A buffer overflow occurs in pytorch v2.7.0 when a PyTorch model consists of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tensor.view-torch.mv() and is compiled by Inductor, leading to a Denial of Service (DoS). |
| In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results. |
| In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error. |
| In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results. |
| In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument. |
| PyTorch before 3.7.0 has a bernoulli_p decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallback_random=True. |
| An issue in the component torch.linalg.lu of pytorch v2.8.0 allows attackers to cause a Denial of Service (DoS) when performing a slice operation. |
