AI

Google Researchers Reveal Every Way Hackers Can Trap, Hijack AI Agents

Mercor, a $10 billion AI startup that works with companies including OpenAI and Anthropic, confirms major data breach

Eightco becomes biggest public Worldcoin holder with $326M bet

SoFi Technologies (SOFI) Stock Dips Following Round-the-Clock Banking Platform Debut

Coinbase’s AI payments system joins Linux Foundation, gathers support from Google, Stripe, AWS and others

AI giants Meta, Microsoft, NVIDIA see stocks amid Iran threat, AI cryptos crash

Ripple Launches First Platform to Manage Corporate Cash and Crypto in One Place

Oracle Fires 30,000 Employees Overnight to Fund $58 Billion AI Data Center Debt

Google Accelerates Post-Quantum Timeline to 2029

Microsoft Made GPT and Claude Work Together—And the Result Beats Every AI Research Tool Out There

Meet A-Evolve: The PyTorch Moment For Agentic AI Systems Replacing Manual Tuning With Automated State Mutation And Self-Correction

Onchain RWA Tops $10 Billion and Tokenized Stocks Hit $1B as Institutional Adoption Grows

NVIDIA AI Unveils ProRL Agent: A Decoupled Rollout-as-a-Service Infrastructure for Reinforcement Learning of Multi-Turn LLM Agents at Scale

Anthropic’s Claude Mythos AI Model Exposed in Major Data Breach

Anthropic’s massive 'Claude Mythos' leak sends software names — and crypto — sharply lower

SoftBank Obtains Massive $40B Loan for Additional OpenAI Investment

OpenAI Sits at the Center of a $1.4 Trillion Capital Loop, Morgan Stanley Warns

Morgan Stanley warns that OpenAI sits at the center of a massive $1.4 trillion capital loop, with infrastructure commitments far exceeding its $13 billion annual revenue. Major tech companies like Microsoft, Amazon, Oracle, and Nvidia are both funding OpenAI and receiving its spending commitments back, creating potential hidden leverage risks for investors.

LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts

Researchers have identified a new vulnerability in large language models called 'natural distribution shifts' where seemingly benign prompts can bypass safety mechanisms to reveal harmful content. They developed ActorBreaker, a novel attack method that uses multi-turn prompts to gradually expose unsafe content, and proposed expanding safety training to address this vulnerability.

Shape and Substance: Dual-Layer Side-Channel Attacks on Local Vision-Language Models

Researchers discovered significant privacy vulnerabilities in local Vision-Language Models that use Dynamic High-Resolution preprocessing. The dual-layer attack framework can exploit execution-time variations and cache patterns to infer sensitive information about processed images, even when models run locally for privacy.

Impact of AI Search Summaries on Website Traffic: Evidence from Google AI Overviews and Wikipedia

A research study analyzing Google's AI Overviews feature found it reduces Wikipedia traffic by approximately 15% through causal analysis of 161,382 matched articles. The impact varies by content type, with Culture articles experiencing larger traffic declines than STEM topics, suggesting AI summaries substitute for clicks when brief answers satisfy user queries.

Decidable By Construction: Design-Time Verification for Trustworthy AI

Researchers propose a framework for verifying AI model properties at design time rather than after deployment, using algebraic constraints over finitely generated abelian groups. The approach eliminates computational overhead of post-hoc verification by building trustworthiness into the model architecture from the start.

AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study

Researchers developed AD-CARE, an AI agent that uses large language models to diagnose Alzheimer's disease from incomplete medical data across multiple modalities. The system achieved 84.9% diagnostic accuracy across 10,303 cases and improved physician decision-making speed and accuracy in clinical studies.

CRAFT: Grounded Multi-Agent Coordination Under Partial Information

Researchers introduce CRAFT, a multi-agent benchmark that evaluates how well large language models coordinate through natural language communication under partial information constraints. The study finds that stronger reasoning abilities don't reliably translate to better coordination, with smaller open-weight models often matching or outperforming frontier systems in collaborative tasks.

The Future of AI-Driven Software Engineering

A paradigm shift is occurring in software engineering as AI systems like LLMs increasingly boost development productivity. The paper presents a vision for growing symbiotic partnerships between human developers and AI, identifying key research challenges the software engineering community must address.

LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends

Researchers have published a comprehensive review of Large Language Models for Autonomous Driving (LLM4AD), introducing new benchmarks and conducting real-world experiments on autonomous vehicle platforms. The paper explores how LLMs can enhance perception, decision-making, and motion control in self-driving cars, while identifying key challenges including latency, security, and safety concerns.