Redis CVE-2026–23479 enables authenticated RCE; affecting versions since 7.2.0, patched May 5 to reduce exploitation risk.
Category: robotics/AI – Page 3
New ‘HTTP/2 Bomb’ DoS attack crashes web servers in under a minute
A new denial-of-service (DoS) attack dubbed HTTP/2 Bomb can be launched from a single machine to take down web servers within seconds.
The technique works on default HTTP/2 configurations of major web servers, including NGINX, Apache HTTP Server, Microsoft IIS, Envoy, and Cloudflare Pingora.
Discovered by OpenAI’s Codex software agent under the guidance of researchers at offensive security firm Calif, HTTP/2 Bomb combines two previously known HTTP/2 DoS methods: the HPACK compression amplification and Slowloris-style resource retention via HTTP/2 flow-control stalling.
Google adds Android protection against AI deepfake scam calls
Google is introducing a new Android security feature that will detect and flag phone calls in which scammers use artificial intelligence to impersonate a user’s personal contacts.
Called “fake call detection,” the feature is rolling out globally this month to Android 12 and later devices, starting with Pixel devices, and will be enabled by default.
Once activated, it works automatically when both a caller and recipient are using Phone by Google: when a contact places a call, their device sends a silent, encrypted confirmation signal to the recipient’s device in real time.
Scientists identify a cell type in the brain that was previously ignored and it may explain why human memory has no known upper limit
The human brain contains roughly 86 billion neurons. That number appears in almost every popular account of memory and intelligence, and it tends to carry an implicit argument: that the scale of human cognition follows from the scale of this cell count. What is less often mentioned is that the brain contains a roughly comparable number of a different cell type entirely, one that researchers have treated, for most of the history of neuroscience, as little more than biological scaffolding.
A paper published on 23 May in the Proceedings of the National Academy of Sciences puts forward a new hypothesis about what those cells, called astrocytes, might actually be doing. The work comes from a team at MIT: lead author Leo Kozachkov, Jean-Jacques Slotine, a professor of mechanical engineering and brain and cognitive sciences, and Dmitry Krotov of the MIT-IBM Watson AI Lab, who is the paper’s senior author. Their claim is not that astrocytes have been misunderstood in any dramatic sense; it is the more careful suggestion that they may be doing computational work that neurons, on their own, cannot account for.
This is a hypothesis supported by a mathematical model. The experimental work needed to test it has not yet been done.
The REAL Reason It’s Already Too Late For Most People
A former Google executive says the West is sleepwalking into irrelevance. Mo Gawdat, the former Chief Business Officer at Google X, explains why every nation that fails to build its own AI infrastructure will become a technology colony of the United States and China, dependent on imported intelligence the way developing nations once depended on imported manufacturing.
Mo draws a direct comparison to how China built its tech independence. When Google operated in China, Russian search engine Yandex was protected by the government through regulation that made it difficult for American companies to dominate. The result was that domestic competitors were forced to exist, and they became competitive. He argues the UK and Europe are doing the opposite: importing every piece of software, every AI model, and every platform from Silicon Valley, sending trillions in licensing fees overseas while building nothing domestically.
Discover:
• Why every nation not building its own AI will become \.
AI misses cancer drug target, revealing why lab validation still matters
Researchers at the Icahn School of Medicine at Mount Sinai have identified a previously hidden druggable site in a cancer-related protein that could open the door toward the development of a new generation of more precise cancer drugs. The finding also reveals important limitations in today’s artificial intelligence tools for drug discovery.
The study, published in the June 2 online issue of the Journal of the American Chemical Society, focused on PKMYT1, a type of protein known as a kinase that helps control how cells grow and divide. Because this process can go wrong in cancer, PKMYT1 has emerged as a promising target for new cancer drugs.
Most experimental drugs designed to block kinases work by targeting a region called the ATP-binding site—the part of the protein that uses the cell’s energy supply to function. But many kinases share nearly identical ATP-binding sites, making it difficult for drugs to distinguish between the desired target and other kinases, which can lead to unwanted side effects.
AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning
The problem? Human brains (and animal brains, too) are incredibly complex. While these handcrafted models are great starting points, they often oversimplify things and miss the messy, rich reality of actual behavior. On the flip side, using powerful, flexible AI to analyze data can capture that richness, but AI usually gives us a “black box”—it finds patterns but can’t explain *why* or *how* it found them, leaving scientists to do the heavy lifting of figuring out the rules.
Scientific models are widely used across the natural sciences as an interface between scientific theories and empirical data [1]. Such models play a key role, for example, in the study of human and animal learning, where they express algorithmic hypotheses and relate them to psychology and neuroscience data [2, 3]. These models are traditionally handcrafted by expert researchers based on existing theory or new insights. Such handcrafted models, however, are now known to fall short of capturing the full richness of behavior, even in their narrow domains [4– 7]. An alternative data-driven approach has emerged, seeking to discover new insights by fitting and interpreting flexible models [8– 11]. However, these tools require substantial human effort to derive insight from data, and it has been unclear how to discover new ideas from data efficiently. Here, we present DataDIVER, a general approach for automatically discovering computational models from data, and demonstrate that these models surface novel mechanistic insights into human and animal learning. Our approach delivers models that take the form of short computer programs, which are optimized both to fit data well and to be simple. These programs explicitly connect with existing theoretical frameworks and are readily understandable by human scientists. They can also be used to make novel predictions, some of which we show are borne out in re-analysis of existing data. General-purpose tools for surfacing new ideas from data, especially in combination with the large datasets that are increasingly available in many fields, stand to dramatically accelerate scientific discovery.
The authors have declared no competing interest.
AI system automates scientific software design, outperforming human-written code in key benchmarks
A research team at Google co-led by Michael Brenner, Catalyst Professor of Applied Mathematics and Physics at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Google research scientist, has produced a new artificial intelligence system that can automatically write scientific software programs that surpass the performance of human-written programs. The paper is published in the journal Nature.
How the ERA system came together The system is called Empirical Research Assistance (ERA), and the project was co-led by Brenner and Shibl Mourad from Google DeepMind. Harvard Ph.D. students Qian-Ze Zhu, Ryan Krueger, and Sarah Martinson contributed as Google student researchers while working in Brenner’s group. The research was done in Brenner’s capacity as a Catalyst Professor, a position established by the University to enhance relationships between academia and the private sector by supporting senior faculty in research roles at external companies.
Across modern science, customized software is constantly used to test specific hypotheses or interpret complex data. The authors refer to this type of computer program as “empirical software”—a program whose sole purpose is to maximize how well it does on a scientific task, like making weather predictions or forecasting hospitalizations during a disease outbreak. Any problem that can be expressed as a numerical value—its “score”—is called a scorable task.