AI Sec Watch

CVE-2025-23336 is a vulnerability in NVIDIA Triton Inference Server (software that runs AI models on Windows and Linux) where an attacker could cause a denial of service (making the system unavailable) by loading a misconfigured model. The vulnerability stems from improper input validation (the system not properly checking whether data is safe before using it).

CVE-2025-23329 is a vulnerability in NVIDIA Triton Inference Server (a tool used to run AI models efficiently) on Windows and Linux where an attacker could damage data in memory by accessing a shared memory region used by the Python backend, potentially causing the service to crash. The vulnerability involves improper access control (failing to properly restrict who can access certain resources) and out-of-bounds writing (writing data to memory locations it shouldn't).

CVE-2025-23328 is a vulnerability in NVIDIA Triton Inference Server (software that runs AI models on Windows and Linux) where an attacker could send specially crafted input to cause an out-of-bounds write (writing data outside the intended memory location), potentially causing a denial of service (making the service unavailable). The vulnerability has a CVSS score of 4.0, indicating moderate severity.

NVIDIA Triton Inference Server for Windows and Linux has a vulnerability in its Python backend that allows attackers to execute arbitrary code remotely by manipulating the model name parameter in model control APIs (functions that manage AI models). This vulnerability could lead to remote code execution (RCE, where an attacker runs commands on a system they don't own), denial of service (making the system unavailable), information disclosure (exposing sensitive data), and data tampering (modifying stored information).

NVIDIA Triton Inference Server has a vulnerability in its DALI backend (a component that processes data) where improper input validation (the failure to check if data is safe before using it) allows attackers to execute code on the system. The issue is classified as CWE-20, a common weakness type related to input validation problems.

picklescan is a tool that checks if pickle files (a Python format for storing objects) are safe before loading them, but versions up to 0.0.30 have a vulnerability where attackers can bypass these safety checks by giving a malicious pickle file a PyTorch-related file extension. When the tool incorrectly marks this file as safe and it gets loaded, the attacker's malicious code can run on the system.

This research addresses how to make reinforcement learning (RL, where AI systems learn to make decisions by trial and error) safer for healthcare by proposing a method called Constraint Transformer that learns safety rules from historical medical records instead of requiring real-time interaction. The system uses a causal attention mechanism (a technique that identifies which past events matter most) and a generative world model (a simulation tool) to identify unsafe treatment decisions and improve patient outcomes while reducing harmful behaviors.

n8n, an open source workflow automation platform, has a stored XSS vulnerability (cross-site scripting, where malicious code is saved and runs in users' browsers) in versions 1.24.0 through 1.106.x. An authorized user can inject harmful JavaScript into the initialMessages field of the LangChain Chat Trigger node, and if public access is enabled, this code runs in the browsers of anyone visiting the public chat link, potentially allowing attackers to steal cookies or sensitive data through phishing.

A ReDoS vulnerability (regular expression denial of service, where specially crafted input causes a program's pattern-matching code to consume excessive CPU) was found in the Hugging Face Transformers library's number normalization feature. An attacker could send text with long digit sequences to crash or slow down text-to-speech and number processing tasks. The vulnerability affects versions up to 4.52.4.