| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Insecure Default Initialization of Resource vulnerability allows Authentication Bypass via API access. This issue affects Pandora FMS: from 777 through 800 |
| The issue was addressed with improved input validation. This issue is fixed in iOS 18.7.9 and iPadOS 18.7.9, iOS 26.5 and iPadOS 26.5, macOS Tahoe 26.5, tvOS 26.5, visionOS 26.5, watchOS 26.5. Processing maliciously crafted web content may lead to an unexpected process crash. |
| The issue was addressed with improved UI handling. This issue is fixed in iOS 26.5 and iPadOS 26.5, macOS Tahoe 26.5, visionOS 26.5. A malicious iframe may use another website’s download settings. |
| This issue was addressed by adding an additional prompt for user consent. This issue is fixed in iOS 18.7.9 and iPadOS 18.7.9, iOS 26.5 and iPadOS 26.5, macOS Sequoia 15.7.7, macOS Sonoma 14.8.7, macOS Tahoe 26.5, visionOS 26.5. An app may be able to access user-sensitive data. |
| A use after free issue was addressed with improved memory management. This issue is fixed in iOS 18.7.9 and iPadOS 18.7.9, iOS 26.5 and iPadOS 26.5, macOS Sequoia 15.7.7, macOS Sonoma 14.8.7, macOS Tahoe 26.5, tvOS 26.5, visionOS 26.5, watchOS 26.5. A remote attacker may be able to cause unexpected system termination or corrupt kernel memory. |
| In the Linux kernel, the following vulnerability has been resolved:
spi: stm32-ospi: Fix resource leak in remove() callback
The remove() callback returned early if pm_runtime_resume_and_get()
failed, skipping the cleanup of spi controller and other resources.
Remove the early return so cleanup completes regardless of PM resume
result. |
| OpenTelemetry.OpAmp.Client is the OpAMP client for OpenTelemetry .NET. Prior to 0.2.0-alpha.1, when receiving responses from the OpAMP server over HTTP, the OpAMP client allocates an unbounded buffer to read all bytes from the server, with no upper-bound on the number of bytes consumed. This could cause memory exhaustion in the consuming application if the configured OpAMP server is attacker-controlled (or a network attacker can MitM the connection) and an extremely large body is returned in the response. This vulnerability is fixed in 0.2.0-alpha.1. |
| The issue was addressed with improved memory handling. This issue is fixed in iOS 26.5 and iPadOS 26.5, macOS Sequoia 15.7.7, macOS Sonoma 14.8.7, macOS Tahoe 26.5, tvOS 26.5, visionOS 26.5, watchOS 26.5. Processing a maliciously crafted image may corrupt process memory. |
| A privacy issue was addressed with improved checks. This issue is fixed in iOS 26.5 and iPadOS 26.5. A user may be able to view restricted content from the lock screen. |
| The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the Trainer.load() method of the Trainer class. The method loads model checkpoint files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method. |
| The snorkel library thru v0.10.0 contains a critical insecure deserialization vulnerability (CWE-502) in the BaseLabeler.load() method of the BaseLabeler class. The method loads serialized labeler models using the unsafe pickle.load() function on user-supplied file paths without any validation or security controls. Python's pickle module is inherently dangerous for deserializing untrusted data, as it can execute arbitrary code during the deserialization process. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method. |
| The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the MultitaskClassifier.load() method of the MultitaskClassifier class. The method loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method. |
| An arbitrary file upload vulnerability in MK-Auth 23.01K4.9 allows attackers to execute arbitrary code via uploading a crafted PHP file. |
| The torch-checkpoint-shrink.py script in the ml-engineering project in commit 0099885db36a8f06556efe1faf552518852cb1e0 (2025-20-27) contains an insecure deserialization vulnerability (CWE-502). The script uses torch.load() to process PyTorch checkpoint files (.pt) without enabling the security-restrictive weights_only=True parameter. This oversight allows the deserialization of arbitrary Python objects via the pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution in the context of the user running the script. |
| The nexent v1.7.5.2 backend service contains an unauthorized arbitrary file deletion vulnerability in its ElasticSearch service interface. The DELETE /{index_name}/documents endpoint lacks proper authentication and authorization controls and does not validate the user-supplied path_or_url parameter. This allows unauthenticated remote attackers to send crafted requests that trigger the deletion of arbitrary documents from ElasticSearch indices and corresponding files from the MinIO storage system. Successful exploitation leads to data destruction and denial of service. |
| The nexent v1.7.5.2 backend service contains an unauthorized arbitrary storage file deletion vulnerability in its file management API. The DELETE /storage/{object_name:path} endpoint lacks authentication, authorization, and input validation mechanisms. Unauthenticated remote attackers can send crafted requests with a user-controlled object_name path parameter to delete arbitrary files from the underlying MinIO storage system. Successful exploitation leads to data loss and denial of service. |
| The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) allows arbitrary code execution. When a user supplies a directory path via the --model command-line argument, the function reads a module.py file from that directory and executes its contents directly using Python's exec() function. This design does not validate or sanitize the file's content, allowing an attacker who controls the input directory to execute arbitrary Python code in the context of the process running the script. |
| The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When a user provides a single model file path (e.g., .pt or .pth) via the --model command-line argument, the function loads the file using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution during deserialization on the victim's system. |
| PySyft (Syft Datasite/Server) versions 0.9.5 and earlier are vulnerable to remote code execution due to insufficient validation and sandboxing of user-submitted code. The system allows low-privileged users to submit Python functions (via @sy.syft_function()) for remote execution on the server. While a code approval mechanism exists, the submitted code undergoes no security checks for dangerous operations (e.g., file access, command execution). Once approved, the code is executed within the server process using exec() and eval() functions without proper isolation. A remote attacker can leverage this to execute arbitrary Python code on the server, leading to complete compromise of the server environment. |
| Missing authorization in the PAM module in Devolutions Server allows an authenticated user with a PAM license but no additional permissions to obtain OTP secret keys and recovery codes via crafted requests to PAM API endpoints.
This issue affects the following versions :
*
Devolutions Server 2026.1.6.0 through 2026.1.11.0
*
Devolutions Server 2025.3.16.0 and earlier |
