AI Sec Watch

A vulnerability in Hugging Face Transformers' Transformer-XL model allows attackers to run arbitrary code (remote code execution) on a victim's computer by tricking them into opening a malicious file or visiting a malicious webpage. The flaw occurs because the software doesn't properly validate data when reading model files, allowing attackers to exploit the deserialization process (converting saved data back into objects that the program can use) to inject and execute malicious code.

A vulnerability in Hugging Face Transformers' Perceiver model allows attackers to run malicious code on a user's computer by tricking them into opening a malicious file or visiting a harmful webpage. The flaw happens because the software doesn't properly check data when loading model files, allowing untrusted code to be executed (deserialization of untrusted data, where a program reconstructs objects from stored data without verifying they're safe).

Tencent HunyuanDiT (an AI image generation model) has a remote code execution vulnerability in its model_resume function that allows attackers to run arbitrary code if a user opens a malicious file or visits a malicious page. The flaw stems from improper validation of user input during deserialization (converting data from storage format back into usable objects), allowing attackers to execute code with root-level privileges.

CVE-2025-63664 is a flaw in the GT Edge AI Platform (before version 2.0.10-dev) where incorrect access control in the /api/v1/conversations/*/messages API allows attackers without permission to view other users' message histories with AI agents. This is classified as improper access control (CWE-284, a category of security flaws where systems fail to properly restrict what users can access).

Cyberbullying on social media is a growing problem that harms people's mental health, and traditional methods to stop it are no longer effective. This study examines how artificial intelligence can help protect online communities from cyberbullying by exploring different AI technologies, their uses, and the challenges involved. The goal is to understand how AI might create safer online environments.

Generative AI (systems that create new text, images, or other content) is transforming many industries but raises ethical concerns like data privacy (protecting personal information), bias (unfair treatment of certain groups), transparency (being open about how the AI works), and accountability (responsibility for the AI's actions). Researchers propose a trust framework based on transparency, fairness, accountability, and privacy to help ensure generative AI is developed and used responsibly.

Large language models (LLMs, AI systems trained on huge amounts of text to generate human-like responses) can now mimic not just general human language but also unusual, individual-specific human behaviors. This ability could lead to LLMs being used more widely in research studies and potentially reduce the role of actual humans, which raises concerns about AI alignment (ensuring AI systems behave in ways humans intend and approve of) and how this technology affects society.

This research addresses a problem in federated learning (a method where multiple computers train an AI model together without sharing raw data) combined with adversarial training (a technique that makes AI models resistant to intentionally tricky inputs). The authors found that simply combining these two approaches causes the model's accuracy to drop because adversarial training increases differences in the data across different computers, making the federated learning less effective. They propose SFAT (Slack Federated Adversarial Training), which uses a relaxation mechanism to adjust how the computers combine their learning results, reducing the harmful effects of data differences and improving overall performance.

This research proposes SIAMD, a framework for detecting social media bots (automated accounts that spread misinformation) before they cause harm. The system analyzes patterns in how user accounts interact with messages, uses structural entropy (a measure of uncertainty in data patterns) to identify bot-like behavior, and generates synthetic bot messages with large language models (AI systems trained on text data) to test and improve detection systems.