AI Sec Watch

Researchers have developed CrossMPI, an image-based prompt injection attack (tricking an AI by hiding instructions in its input) that uses nearly invisible changes to images to manipulate how multimodal AI systems (AI that processes both images and text) interpret user instructions without modifying the text itself. The attack successfully fooled multiple vision-language models (AI systems that understand both images and text) about 66% of the time by targeting the intermediate layers where visual and textual information are combined, posing growing security risks as enterprises increasingly adopt multimodal AI systems.

OpenAI and Dell Technologies are partnering to let businesses use Codex (an AI tool that writes and understands code) in their own private data centers and hybrid environments (networks that combine on-site systems with cloud services) rather than only in the cloud. This allows companies to keep sensitive data secure while using Codex across their existing tools and workflows for both coding tasks and broader business automation.

AI-assisted coding is causing a rapid increase in leaked secrets (authentication credentials and API keys), with AI-related secrets exposed jumping 81% in 2025 alone, because developers prioritize speed and functionality over security reviews. When secrets are discovered, organizations should treat them as security incidents, immediately revoking or disabling the exposed credential, generating a new one, investigating system impact, performing cleanup, and hardening systems, followed by post-mortems to improve processes.

This article covers a legal dispute between Elon Musk and Sam Altman over OpenAI's conversion from a nonprofit to a for-profit company. Musk founded OpenAI in 2015 with Altman to prevent Google from monopolizing AI technology, but sued in 2024 claiming they violated their commitment to keep it nonprofit, while Altman argues no such commitment was ever made. The article focuses on their trial testimony rather than any technical AI issue or security concern.

South Korea is using its upcoming local elections as a test case to see whether laws can effectively stop deepfakes (fake videos or audio created using AI to manipulate what people look like or sound like). The country is examining whether regulation can reduce the spread of these manipulated media during elections.

Google has introduced Gemini Omni Flash, a new AI model that can generate and edit videos from text, images, audio, or video inputs combined together. The model uses reasoning about physics and real-world knowledge to create realistic videos and allows users to edit them through natural language conversation (giving text instructions rather than using traditional editing tools), with changes building on each other while maintaining consistency in characters, physics, and scene details.

A study of 350 MBA students and 42 information systems graduates found that feedback from generative AI (AI systems that create new text or content) has mixed effects on workers: it boosts confidence in their abilities and motivation, but simultaneously makes them feel devalued and replaceable because the AI can perform the same tasks independently. The research also discovered that GenAI creates 'prompt engineering convergence' (where different types of work become repetitive prompting and reviewing tasks), which doesn't motivate workers the way traditional job variety does.

A panel of Australian information systems academics examined how generative AI (GenAI, AI systems that create new text, images, or code based on patterns in training data) is changing higher education, drawing on research from 45 universities across Australia and New Zealand. The panel identified key challenges including learning assurance (ensuring students actually learn the material), privacy, intellectual property rights, and security, while also exploring opportunities for innovation in teaching and research. The discussion emphasized the need for responsible AI governance and policies to guide institutions in adopting GenAI safely and ethically.

A panel of academics discussed how AI is changing their work in teaching, research, and service roles, finding both opportunities to boost productivity and concerns about ethical and professional risks. The impact of AI in academia depends on factors at multiple levels, including individual understanding of AI, how institutions govern its use, and discipline-specific practices. The researchers recommend that Information System scholars study human-AI collaboration, build trustworthy AI tools, and examine how AI affects academics' careers.