AI Sec Watch

Mark Zuckerberg, Larry Ellison, Jensen Huang, and Sergey Brin have been named to the President's Council of Advisors on Science and Technology (PCAST), a new advisory panel that will provide input on AI policy and other technology matters to the U.S. President. The panel will start with 13 members but could expand to 24, and will be co-chaired by David Sacks and Michael Kratsios.

Two versions of LiteLLM (a Python library for working with multiple AI models), versions 1.82.7 and 1.82.8, were published with malware that steals user credentials (usernames, passwords, and authentication tokens). This is a critical security issue because anyone who installed these specific versions could have their sensitive login information compromised.

Connected autonomous vehicles (CAVs) use multiple types of sensors, like LiDAR (light-based radar that creates 3D maps) and cameras, to understand their surroundings, and combining information from both sensors improves accuracy. However, this sensor fusion process can leak private information and relies on a third party to generate random numbers, which could be compromised by attackers. Researchers propose MPOF, a model that uses secure computation protocols (mathematical methods that let systems calculate results without exposing raw data) and sacrificial verification (a technique that detects when a third party behaves maliciously) to protect privacy while defending against attacks from that third party.

Semi-supervised learning (SSL, a training method where models learn from both labeled and unlabeled data) is vulnerable to backdoor attacks, where attackers can corrupt model predictions by poisoning a small portion of training data with hidden triggers. This paper reveals that SSL backdoor attacks are particularly dangerous because they exploit the pseudo-labeling mechanism (the process where the model assigns labels to unlabeled data) to create stronger trigger-target correlations than in supervised learning. The researchers propose Backdoor Invalidator (BI), a defense framework using complementary learning, trigger mix-up, and dual domain filtering to obstruct and filter backdoor influences during both feature learning and data processing.

Deep neural networks (machine learning models with many layers that process information) are vulnerable to adversarial examples, which are inputs slightly modified to fool the AI into making wrong predictions. This paper proposes adding a redundant fully connected layer (a type of neural network component that connects all inputs to all outputs) with a special loss function to make these networks more robust against attacks while maintaining accuracy on normal inputs.

This research presents a new framework called Propose-Rectify that helps detect and locate image manipulations (alterations made to photos) by combining two approaches: first, a semantic reasoning stage uses a modified LLaVA model (a multimodal AI that understands both images and language) to identify suspicious regions, and second, a refinement stage uses specialized forensic analysis (technical methods that detect tampering traces) to validate and precisely locate the manipulated areas. The framework bridges the gap between AI understanding and forensic detection, achieving better accuracy than previous methods.

Harvey, a legal AI startup founded in 2022, raised $200 million at an $11 billion valuation to deploy AI technology in specialized legal and professional services markets. The company uses AI tools to help lawyers with contract analysis, compliance, and other complex tasks, serving over 100,000 lawyers across more than 1,300 organizations. Harvey's funding reflects growing investor confidence that specialized AI applications, not just foundational AI models (the underlying systems that power AI tools), can capture significant business value.

Hugo Barra, a former Meta executive, has returned to the company to lead AI development efforts, reflecting Meta's shift in focus from virtual reality to artificial intelligence. Meta is investing heavily in AI infrastructure and acquiring AI agent technology (software designed to perform tasks autonomously) companies like Dreamer, Manus, and Moltbook to compete with rivals like OpenAI and Google. The company is spending up to $135 billion this year on capital expenditures, mostly for AI infrastructure, as it attempts to develop a competitive strategy in the rapidly evolving AI market.

OpenAI shut down its Sora short-form video app, which had reached one million downloads in its first five days before being discontinued six months later. The company is closing the app as part of cost-cutting efforts while preparing for a potential public offering, and will soon provide a timeline for users to preserve their work from the platform.