#Artificial Intelligence26
AI browsers raise security concerns
A University of Washington study warns that powerful AI agents in web browsers can break a core web security rule and expose user data. Researchers tested several browsers and describe prompt injection and memory poisoning as the main risks.
Photo by Zoshua Colah, Unsplash
AI models encode real-world plausibility
Researchers at Brown University tested whether modern AI language models can tell if events are common, unlikely, impossible or nonsensical. They used mechanistic interpretability and found internal patterns that match human judgments in several open-source models.
Researchers find 'vibe coding' linked to insecure AI-written code
A research team found that a programming style called "vibe coding" is producing insecure code with help from generative AI tools. A new radar scans public vulnerability data and flags cases that show AI signatures or risky patterns.
Brain predictions use phrases, not just next words
New research shows the human brain anticipates upcoming language by grouping words into grammatical phrases rather than predicting only the next single word. Scientists used brain recordings, behavioral tests and LLM measures across languages.
Reducing unsafe responses in large language models
Researchers studied how large language models (LLMs) handle safety and tested training methods to reduce unsafe outputs while keeping performance. They identified key challenges and a technique that preserves safety during fine-tuning.
Human intelligence arises from coordinated brain networks
Researchers used neuroimaging and two adult datasets to test the Network Neuroscience Theory. They found that general intelligence reflects system-level organization and coordination across large-scale brain networks, with implications for development, injury and artificial systems.