Security News This Week: The Cybersecurity Apocalypse Is Coming in'Months,' AI Giants Warn Plus: Hackers target over 100 US water systems, ICE puts in an order for robot dogs, and you'll never guess what "MrChildPorn" was arrested for. You may have noticed that Flock Safety's automatic license plate reader cameras--and the cops who misuse them--are getting a lot of coverage lately. This week, WIRED found a particularly wild case: A cop in Alpharetta, Georgia, was accused of searching for the license plate of a coworker dozens of times after an affair between the two ended, according to internal documents obtained by WIRED. The same police department where the former lovers worked also shared the data captured from its Flock cameras with more than 2,000 police departments, colleges, and other organizations across the United States, and accessed data from more than 1,300 entities in exchange. Also at the intersection of love and surveillance, background-check company PeopleFinder is making use of its extensive dossiers on people to start a new dating site called Stud or Dud.

Alpharetta, Georgia, cops share data with thousands of Flock users, ranging from federal agencies to a fish and wildlife commission. The reasons why show how vast--and invasive--the network has become. Alpharetta, a prosperous suburb of Atlanta, Georgia, is home to about 67,000 people, served by about 120 local police officers. It also hosts several dozen cameras sold by Flock Safety, the increasingly controversial surveillance company that collects license plate data and other information and makes it searchable by police. WIRED has made this article free for all to read because it is primarily based on reporting from public records requests. Please consider subscribing to support our journalism.

After an affair with a fellow police officer ended, a Georgia cop used Flock to track her movements--and those of a man whose vehicle often showed up near hers, internal investigation records show. In recent months, there has been a surge in reports of police officers misusing automatic license plate readers (ALPRs) sold by Flock Safety to stalk former romantic partners--but details about these incidents are often scarce. Now, with documents obtained via a public records request, WIRED can reveal specific details about one extensive Flock-fueled surveillance campaign and the subsequent investigation and fallout. Dustin Bozzo, who until this month worked as a patrolman in the Atlanta-area suburb of Alpharetta, Georgia, allegedly used Flock 56 times between March and May to search for the license plate of a former romantic partner--a fellow member of the Alpharetta Police Department. When Bozzo noticed that his ex's car was often near that of another police officer, he began searching for that officer's vehicle as well.

One person has received medical treatment after a passenger's laptop caught fire on an American Airlines flight. The pilot had reported a cabin fire in the back of the airplane to air traffic control, saying the flight attendants are trying to put it out now, according to audio obtained by the BBC's US reporting partner CBS News. I think we have about four to five passengers that were burned by the laptop. The laptop is contained about mid-aircraft. One passenger received medical attention on site and was released after American Airlines Flight 2398 - coming from Atlanta, Georgia - landed at Dallas Fort Worth International Airport in Texas on Friday.

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Ballots Have Been Seized Across the US. So far this year, authorities have seized or demanded ballots from elections in four states. Experts fear the trend could throw the midterms into chaos unless courts draw a line. As US voters look to the November midterms, the Trump administration is obsessed with looking back to past elections, seizing ballots cast years ago in several states in search, it claims, of fraud or other malfeasance. But experts believe the goal may be more varied. The seizures began in January when FBI agents armed with a warrant raided an election facility in Fulton County, Georgia, and grabbed 600 boxes of ballots from 2020.

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ChatGPT predicted the first round of the NFL Draft and here's what it said Curt Cignetti was so focused this offseason, he turned down all external requests: 'I'm 95% football' Former MLB owner claims'despicable' San Francisco Giants are the reason the A's left Oakland Longtime NASCAR crew chief tells wild story about one of the sport's biggest characters WNBA finally embraces Caitlin Clark's stardom with unprecedented national TV schedule Why are the Mets so bad? Flyers mascot Gritty pens letter to fans ahead of first playoff game... eight years after he debuted NFL Draft prospect Rueben Bain Jr. mum about 2024 crash when publicly asked about it for first time Troy Aikman is selling'fire suites,' which are exactly what they sound like Trump says there's'no time frame' to secure Iran deal Iranian activist praises Trump's intervention after female protesters saved from execution Steve Hilton praised for'offering solutions' in CA gubernatorial debate Middle East tensions escalate over US blockade, Iran's actions Michael Easter and Gary Brecka discuss the'choice' to live to be 100 Sen Ted Cruz calls new deadline with Iran'really consequential' OutKick Charles Barkley was disgusted by Magic's highly questionable pregame handshake After Stephen A. Smith's controversial comments about Memphis sparked backlash, Barkley stepped in and delivered a blunt response. The debate quickly turned into a heated clash over media responsibility, perception, and what crosses the line. The Orlando Magic dropped Game 2 of their first-round playoff series to the Detroit Pistons on Wednesday, and it's safe to assume Charles Barkley was mighty confident that the L was coming before the game even tipped off. In the lead-up to the matchup in Detroit, the ESPN broadcast shared a live shot of members of the Magic partaking in some pre-game antics.

Inference-time LLM alignment methods, particularly activation steering, offer an alternative to fine-tuning by directly modifying activations during generation. Existing methods, however, often rely on non-anticipative interventions that ignore how perturbations propagate through transformer layers and lack online error feedback, resulting in suboptimal, open-loop control. To address this, we show empirically that, despite the nonlinear structure of transformer blocks, layer-wise dynamics across multiple LLM architectures and scales are well-approximated by locally-linear models. Exploiting this property, we model LLM inference as a linear time-varying dynamical system and adapt the classical linear quadratic regulator to compute feedback controllers using layer-wise Jacobians, steering activations toward desired semantic setpoints in closed-loop with minimal computational overhead and no offline training. We also derive theoretical bounds on setpoint tracking error, enabling formal guarantees on steering performance. Using a novel adaptive semantic feature setpoint signal, our method yields robust, fine-grained behavior control across models, scales, and tasks, including state-of-the-art modulation of toxicity, truthfulness, refusal, and arbitrary concepts, surpassing baseline steering methods. Our code is available at: https://github.com/trustworthyrobotics/lqr-activation-steering

We study signal propagation at initialization in transformers through the averaged partial Jacobian norm (APJN), a measure of gradient amplification across layers. We extend APJN analysis to transformers with bidirectional attention and permutation-symmetric input token configurations by deriving recurrence relations for activation statistics and APJNs across layers. Our theory predicts how attention modifies the asymptotic behavior of the APJN at large depth and matches APJNs measured in deep vision transformers. The criticality picture known from residual networks carries over to transformers: the pre-LayerNorm architecture exhibits power-law APJN growth, whereas transformers with LayerNorm replaced by elementwise $\tanh$-like nonlinearities have stretched-exponential APJN growth, indicating that the latter are subcritical. Applied to Dynamic Tanh (DyT) and Dynamic erf (Derf) transformers, the theory explains why these architectures can be more sensitive to initialization and optimization choices and require careful tuning for stable training.