| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| The issue was addressed with improved memory handling. This issue is fixed in iOS 26.5 and iPadOS 26.5, macOS Tahoe 26.5, visionOS 26.5. Processing maliciously crafted web content may lead to an unexpected process crash. |
| 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. |
| An authorization issue was addressed with improved state management. This issue is fixed in iOS 15.8.4 and iPadOS 15.8.4, iOS 16.7.11 and iPadOS 16.7.11, iOS 18.3.1 and iPadOS 18.3.1, iPadOS 17.7.5. A physical attack may disable USB Restricted Mode on a locked device. Apple is aware of a report that this issue may have been exploited in an extremely sophisticated attack against specific targeted individuals. |
| 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. |
| Allocation of Resources Without Limits or Throttling vulnerability in Apache Tomcat.
This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117.
Older, unsupported versions may also be affected.
Users are recommended to upgrade to version [FIXED_VERSION], which fixes the issue. |
| Improper Neutralization of Special Elements used in an SQL Command vulnerability allows SQL Injection via graph container parameter. This issue affects Pandora FMS: from 777 through 800 |
| Server-Side Request Forgery vulnerability allows Privilege Escalation via API Checker extension. This issue affects Pandora FMS: from 777 through 800 |
| In uriparser before 1.0.2, there is pointer difference truncation to int in various places. |
| Session Fixation vulnerability allows Session Hijacking via crafted session ID. This issue affects Pandora FMS: from 777 through 800 |
| Cross-Site Request Forgery vulnerability allows an attacker to perform unauthorized actions via crafted web page. This issue affects Pandora FMS: from 777 through 800 |
| The HTTP/2 protocol allows a denial of service (server resource consumption) because request cancellation can reset many streams quickly, as exploited in the wild in August through October 2023. |
| Insecure Default Initialization of Resource vulnerability allows Authentication Bypass via API access. This issue affects Pandora FMS: from 777 through 800 |
| In the Linux kernel, the following vulnerability has been resolved:
drm/xe/pxp: Clear restart flag in pxp_start after jumping back
If we don't clear the flag we'll keep jumping back at the beginning of
the function once we reach the end.
(cherry picked from commit 0850ec7bb2459602351639dccf7a68a03c9d1ee0) |
| PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting 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 checkpoint file, leading to arbitrary code execution on the victim's system when the file is loaded. |
| 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. |
| 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. |
| 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 loading a model state dictionary from a state_dict.pt file via torch.load(), the function does not enable 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 state_dict.pt file within a directory specified via the --model argument, leading to arbitrary code execution during the deserialization process on the victim's system. |
| 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 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 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. |
