VAITP

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 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.

Microdot is a minimalistic Python web framework. Prior to 2.6.1, the Response.set_cookie() method does not sanitize its string arguments, and in particular will not detect the presence of the \r\n sequence in them. This can be a potential source of header injection attacks. For a header injection attack through this issue to be possible, an attacker must first infiltrate the client (for example through an independent XSS attack), so that it can send malicious information that is destined to be stored in a cookie by the server on behalf of the victim. An attacker that infiltrates one client can only orchestrate a header injection attack for that client, all other clients that were not infiltrated are safe. This vulnerability is fixed in 2.6.1.

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev100, pyload-ng WebUI returns full Python traceback details to clients on unhandled exceptions. Because /web/<path:filename> is reachable without authentication and renders attacker-controlled template names, an unauthenticated user can reliably trigger a server exception (for example by requesting a non-existent template) and receive internal stack traces in the HTTP response. This vulnerability is fixed in 0.5.0b3.dev100.

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev100, when passing a folder name in the set_package_data() API function call inside the data object with key "_folder", there is no sanitization at all, allowing a user with Perms.MODIFY to specify arbitrary directories as download locations for a package. This vulnerability is fixed in 0.5.0b3.dev100.

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev100, package folder names are sanitized using insufficient string replacement. The pattern ....// becomes .._ after replacement (partial removal), leaving .. which can be exploited when the path is later resolved by the OS. This vulnerability is fixed in 0.5.0b3.dev100.