找到 194 篇相关文章
Over the last couple of weeks, hackers have targeted and broken into the systems of several water plants in the United States. Here’s what we know and don’t know about this wave of attacks allegedly carried out by the Iranian government.
The new order sweeps away decades of existing U.S. cybersecurity policy prohibiting private companies from conducting 'hack back' attacks or offensive cyber operations.
The data was scraped and exfiltrated from 2,500 users of a compromised AI package.
Mesh, an AI-powered contacts app and relationship manager from Automattic, is now an Android app.
Form Energy has landed Google and Crusoe as customers. Now, it has raised $750 million to expand manufacturing to deliver its massive, 100-hour batteries.
For a long time, my mental model of Dependency Injection looked like this: create an interface,...
The Road to Battlefield competition is now in its second year, and its purpose is to give founders across Central Eurasia a direct route to Startup Battlefield
Security researchers found that in less than 60 seconds, they could open a hatch on a plane’s exterior, plug in a tiny device, and redirect the aircraft’s autopilot or sabotage its flight plan.
Researchers say it took fewer than 20 prompts for a public AI tool to find a flaw (now fixed) allowing anyone on a Zoom call to hijack another participants’ device.
A car mask can pass review and still teach the model to keep the wrong pixels. The outline looks clean. The bumper is inside. The wheels are inside. Then the trained network holds onto the dark patch under the tires, because the label treated that patch as part of the vehicle's visual neighborhood. Training stays quiet. Production gets loud the first time a listing photo drags a strip of the old lot onto a new backdrop. AutoLensAI turns dealer photography into listing-ready vehicle media. This installment follows the earlier pieces on segmentation and image provenance, then narrows to one question: how do I teach a matting model that the shadow touching a tire is evidence against foreground rather than a faint version of it? 1. The failure arrives without an error message Vehicle matting estimates which pixels belong to the vehicle, at finer boundary resolution than segmentation gives. Tires, rocker panels, glossy showroom floors, and the halo under a lowered front lip are where a pretty binary mask does its damage. Two cases cause most of it. A cast shadow can touch rubber and still sit outside the object. A reflection can match paint color exactly and still belong to the floor. Both look like they belong to the car in a thumbnail. Neither belongs to it in geometry. A binary target has no vocabulary for that distinction. Every pixel is in or out, so the annotator's only lever is where to put the line. Push the line outward and shadow becomes vehicle. Pull it inward and the wheel arch loses its edge. Neither answer says the thing that matters, which is that some exterior pixels are ordinary background and some are adversarial background sitting one pixel from the object. The model learns the difference anyway. It learns it wrong, because nothing in the supervision ever separated the two. 2. Three states, not two The supervision contract uses three: state meaning training treatment vehicle body, glass, wheels, trim, and visible geometry foreground loss hard negative
Battery materials startup Sila will use a $1.4 billion loan from the U.S. Department of Defense to scale production at its factory in Washington State.
Two-thirds of an octopus’s neurons are in its arms—each operating independently—including the one it uses to have sex.
If you want to capture the sun’s power with solar panels, these are the best solar generators I’ve tested.
Framework told "all" of its customers that hackers accessed their names, email addresses, phone numbers, and physical addresses in a data breach.
Security researchers tracked and eavesdropped on a WIRED reporter using vulnerabilities in a pink plastic smartwatch. It’s just one piece of a deeply insecure supply chain of GPS-enabled gadgets.
Dumanshu Goyal discusses optimizing data layers for low-latency workloads like AI feature stores. Drawing lessons from NASA's Space Shuttle, he explains how proxy architectures introduce hidden CPU costs, elevated tail latencies, and blast-radius risks. He demonstrates how direct-access Valkey architectures achieve microsecond latency, improve resilience, and slash infrastructure costs. By Dumanshu Goyal
At the Black Hat security conference, the AI giant revealed new details about how its agents went rogue, hacked several other companies—and did it all right under the company’s nose.
Researchers at security firm Zenity found more than a dozen flaws in AI browsers—and managed to get OpenAI’s Atlas to make an unauthorized Amazon purchase.
For nearly two years, researcher Vangelis Stykas has maintained access to North Korean hackers’ servers. His work shows they pulled off intrusions in a shocking number of systems across the globe.
Security researcher James Kettle tried to push the limit of AI’s hacking abilities—and discovered how effective it can be when combined with human expertise.