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A researcher found that using Anthropic's Claude Opus 4.7, he could break into the website of Front Gate--used by every festival from Lollapalooza to Bonnaroo--and freely issue any ticket he chose. Fears about AI tools capable of autonomous hacking usually involve nightmare scenarios like the theft of nuclear launch codes or zeroed-out bank reserves. Far more plausible, it turns out, is asking AI to gain super-administrator access on a ticketing website and then issuing yourself and all of your friends free VIP backstage passes to Bonnaroo. That was the discovery of security researcher Ian Carroll, who used the AI tool Claude Opus 4.7 in April to discover a technique that allowed him full access to the systems of Front Gate Tickets, which handles ticketing for practically every major US music festival, from Lollapalooza and South by Southwest to Austin City Limits. Carroll found that Front Gate, which like Ticketmaster is a subsidiary of the event company Live Nation Entertainment, had a bug in its website that he--with Claude's help--could exploit to gain access to millions of customer or staff records and freely issue tickets for any event, of any value, to himself or whoever he chose.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Debt collection letter for debt you don't owe? China's brain chip breakthrough raises big questions Should you change your phone number after a hack? McDonald's AI drive-thru may take your next order The Father's Day gift that protects your dad from scammers Sheriff's department uses drone to take knife away from suspect inside his home Quantum computing's threat to encryption explained Alleged UFC terror plot suspect called planned attack a'bloodbath' Lou Basenese urges investors to'buy every chip dip' amidst tech sell-off New Air Force One'flying palace' gifted by Qatar unveiled for President Trump Kevin O'Leary warns U.S. must accelerate data center growth to keep pace with China in AI race Kurt'CyberGuy' Knutsson warns fans about World Cup ticket scams involving fake FIFA websites, social media ads for fake tickets and AI-generated job offers and interviews.

From fake tickets to cloned websites, AI is magnifying World Cup scams. Can fans distinguish between what's real and what's not? You got a World Cup ticket. It arrived in your inbox with a QR code, professional branding, and a confirmation email that looked like the real thing. For years, spotting a scam was relatively simple.

This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development.

Improving the performance of deep networks in data-limited regimes has warranted much attention. In this work, we empirically show that "winning tickets" (small subnetworks) obtained via magnitude pruning based on the lottery ticket hypothesis [1], apart from being sparse are also effective recognizers in data-limited regimes. Based on extensive experiments, we find that in low data regimes (datasets of 50-100 examples per class), sparse winning tickets substantially outperform the original dense networks. This approach, when combined with augmentations or fine-tuning from a self-supervised backbone network, shows further improvements in performance by as much as 16% (absolute) on low sample datasets and longtailed classification. Further, sparse winning tickets are more robust to synthetic noise and distribution shifts compared to their dense counterparts. Our analysis of winning tickets on small datasets indicates that, though sparse, the networks retain density in the initial layers and their representations are more generalizable.

The lottery ticket hypothesis (LTH) [20] states that learning on a properly pruned network (the winning ticket) improves test accuracy over the original unpruned network. Although LTH has been justified empirically in a broad range of deep neural network (DNN) involved applications like computer vision and natural language processing, the theoretical validation of the improved generalization of a winning ticket remains elusive. To the best of our knowledge, our work, for the first time, characterizes the performance of training a pruned neural network by analyzing the geometric structure of the objective function and the sample complexity to achieve zero generalization error. We show that the convex region near a desirable model with guaranteed generalization enlarges as the neural network model is pruned, indicating the structural importance of a winning ticket. Moreover, when the algorithm for training a pruned neural network is specified as an (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned weights in the hidden layer. With a fixed number of samples, training a pruned neural network enjoys a faster convergence rate to the desired model than training the original unpruned one, providing a formal justification of the improved generalization of the winning ticket. Our theoretical results are acquired from learning a pruned neural network of one hidden layer, while experimental results are further provided to justify the implications in pruning multi-layer neural networks.

Neural Information Processing Systems http://nips.cc/