VAITP

NVIDIA Apex for all platforms contains a vulnerability in a Python component where an attacker could cause a code injection issue by providing a malicious file. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

pyLoad is the free and open-source Download Manager written in pure Python. Prior to version 0.5.0b3.dev91, the parameter add_links in API /json/add_package is vulnerable to SQL Injection. Attackers can modify or delete data in the database, causing data errors or loss. This issue has been patched in version 0.5.0b3.dev91.

skops is a Python library which helps users share and ship their scikit-learn based models. In versions 0.12.0 and below, the Card.get_model does not contain any logic to prevent arbitrary code execution. The Card.get_model function supports both joblib and skops for model loading. When loading .skops models, it uses skops' secure loading with trusted type validation, raising errors for untrusted types unless explicitly allowed. However, when non-.zip file formats are provided, the function silently falls back to joblib without warning. Unlike skops, joblib allows arbitrary code execution during loading, bypassing security measures and potentially enabling malicious code execution. This issue is fixed in version 0.13.0.

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds read by sending a request. A successful exploit of this vulnerability might lead to information disclosure.

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause the shared memory limit to be exceeded by sending a very large request. A successful exploit of this vulnerability might lead to information disclosure.

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds write by sending a request. A successful exploit of this vulnerability might lead to remote code execution, denial of service, data tampering, or information disclosure.

pyLoad is the free and open-source Download Manager written in pure Python. In versions 0.5.0b3.dev89 and below, there is an opportunity for path traversal in pyLoad-ng CNL Blueprint via package parameter, allowing Arbitrary File Write which leads to Remote Code Execution (RCE). The addcrypted endpoint in pyload-ng suffers from an unsafe path construction vulnerability, allowing unauthenticated attackers to write arbitrary files outside the designated storage directory. This can be abused to overwrite critical system files, including cron jobs and systemd services, leading to privilege escalation and remote code execution as root. This issue is fixed in version 0.5.0b3.dev90.

A remote code execution (RCE) vulnerability exists in the ms-swift project version 3.3.0 due to unsafe deserialization in tests/run.py using yaml.load() from the PyYAML library (versions = 5.3.1). If an attacker can control the content of the YAML configuration file passed to the --run_config parameter, arbitrary code can be executed during deserialization. This can lead to full system compromise. The vulnerability is triggered when a malicious YAML file is loaded, allowing the execution of arbitrary Python commands such as os.system(). It is recommended to upgrade PyYAML to version 5.4 or higher, and to use yaml.safe_load() to mitigate the issue.

There is a defect in the CPython “tarfile” module affecting the “TarFile” extraction and entry enumeration APIs. The tar implementation would process tar archives with negative offsets without error, resulting in an infinite loop and deadlock during the parsing of maliciously crafted tar archives. This vulnerability can be mitigated by including the following patch after importing the “tarfile” module:  https://gist.github.com/sethmlarson/1716ac5b82b73dbcbf23ad2eff8b33e1

BentoML is a Python library for building online serving systems optimized for AI apps and model inference. In versions 1.4.0 until 1.4.19, the file upload processing system contains an SSRF vulnerability that allows unauthenticated remote attackers to force the server to make arbitrary HTTP requests. The vulnerability stems from the multipart form data and JSON request handlers, which automatically download files from user-provided URLs without validating whether those URLs point to internal network addresses, cloud metadata endpoints, or other restricted resources. The documentation explicitly promotes this URL-based file upload feature, making it an intended design that exposes all deployed services to SSRF attacks by default. Version 1.4.19 contains a patch for the issue.