Ramp's Sheets AI recently faced a vulnerability allowing unauthorized data exfiltration through malicious formulas. This risk was responsibly disclosed by PromptArmor, prompting Ramp's security team to quickly resolve the issue. The article explores the attack chain and compares it to a similar risk identified in Claude for Excel.

A recent supply chain attack compromised versions 2.6.2 and 2.6.3 of the popular PyPI package 'lightning', injecting malware that steals credentials and other sensitive data. The attack targets multiple cloud environments and sets up persistent hooks in developer tools, posing risks for many users relying on this framework.

A recent study reveals significant inconsistencies in AI models' carbohydrate estimates for food, with some discrepancies large enough to pose serious health risks for people with diabetes. Results show systematic and stochastic errors across popular AI systems, highlighting the need for caution when relying on AI for carb counting.

Large language models (LLMs) are now transforming unstructured data into structured formats, but current benchmarks often overlook crucial output quality aspects. This article introduces the Structured Output Benchmark (SOB), designed to accurately assess the extraction capabilities of LLMs across various data modalities.

This article explores the integration of the NVIDIA TensorRT plugin with Unreal Engine 5's Neural Network Engine. It highlights how utilizing TensorRT for RTX can enhance AI model inferencing on NVIDIA GPUs, improving performance in real-time graphics workflows while providing practical steps for developers to implement it in their projects.

New Yorkers experienced a glimpse of the future this week as Joby Aviation's electric air taxi completed its first test flight between JFK Airport and a Manhattan heliport. With plans for further demonstrations and partnerships with major airlines, the potential for air taxi services in the city is increasingly becoming a reality.

Zig enforces a strict anti-LLM policy to prioritize the development of new contributors over automated contributions. By rejecting LLM-assisted pull requests, the project aims to foster a community of skilled developers, ensuring that time spent reviewing work contributes to mentoring and growth rather than solely code improvement.

Zig is a programming language gaining traction for its comptime features, appealing particularly to functional programmers. This article explores how Zig addresses the challenges of expressing ideas, building correct systems, and minimizing unexpected outcomes, while questioning the future of garbage collection in modern programming languages.

IBM's Granite 4.1 introduces a new line of open source language models designed for enterprise applications. With three sizes and significant improvements in training quality, the 8B model demonstrates superior performance compared to its 32B predecessor, thanks to focused data handling and a unique filtering approach.

Bringing a hardware product to market in the US requires navigating FCC testing regulations. With 591 accredited labs worldwide, this article outlines where they are located, their accreditation status, and the implications of upcoming FCC proposals that could impact testing facilities in China and Hong Kong.

PostgreSQL 19 introduces several significant enhancements, including in-core plan advice for improved query planning, a new DDL extraction function, and online maintenance features that reduce downtime. Enhanced observability tools allow for better monitoring of lock contention and recovery states, streamlining database management and diagnostics.

This article examines a data-driven project analyzing Airbnb listings across 119 cities, utilizing advanced image processing models to identify suspicious photos and unusual reviews. Highlighting the techniques used and the challenges encountered, it offers insights into automated validation of online rental platforms.

The paper examines the growing challenges of designing dependable autonomous systems in the context of AI and machine learning. It discusses new methodologies and frameworks aimed at addressing the complexities of safety, security, and reliability, focusing on how to manage the inherent uncertainties of intelligent systems.

HealthFormer is a generative model that simulates human physiological changes over time, trained on extensive data from the Human Phenotype Project. It accurately predicts individual health outcomes and simulates the effects of clinical interventions, promising advancements in personalized medicine and clinical decision-making.

DEFault++ introduces a new approach to fault detection in transformer models by classifying and diagnosing issues at multiple abstraction levels. The method achieves high accuracy through a dedicated benchmark and enhances repair decision-making for developers, significantly improving their effectiveness in addressing faults in AI systems.