From small publishers to US federal agencies, websites are reporting unusual spikes in automated traffic linked to IP addresses in Lanzhou, China. For a brief moment in October, Alejandro Quintero thought he had made it big in China . The Bogotá-based data analyst owns and manages a website that publishes articles about paranormal activities, like ghosts and aliens. The content is written in "Spanglish," he says, and was never intended for an Asian audience. But last fall, Quintero's site suddenly began receiving a large volume of visits from China and Singapore.

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New data shows AI bots pushing deeper into the web, prompting publishers to roll out more aggressive defenses. The viral virtual assistant OpenClaw--formerly known as Moltbot, and before that Clawdbot--is a symbol of a broader revolution underway that could fundamentally alter how the internet functions. Instead of a place primarily inhabited by humans, the web may very soon be dominated by autonomous AI bots. A new report measuring bot activity on the web, as well as related data shared with WIRED by the internet infrastructure company Akamai, shows that AI bots already account for a meaningful share of web traffic. The findings also shed light on an increasingly sophisticated arms race unfolding as bots deploy clever tactics to bypass website defenses meant to keep them out.

The first step is always to figure out how easy or hard the VPN is to use. Windscribe and other VPNs are important tools, but you'll never use them if the UI gets in the way. I tested Windscribe's desktop apps on Windows and Mac, its mobile apps on iOS and Android and its Chrome and Firefox browser extensions. To start with, let me say that installing Windscribe is a breeze no matter where you do it. The downloaders and installers handle their own business, only requiring you to grant a few permissions. The apps arrive on your system ready to use out of the box.

Search traffic to news sites has already plunged by a third in one year, according to the Reuters Institute for the Study of Journalism. Search traffic to news sites has already plunged by a third in one year, according to the Reuters Institute for the Study of Journalism. Publishers fear AI search summaries and chatbots mean'end of traffic era' Media companies expect web traffic to their sites from online searches to plummet over the next three years, as AI summaries and chatbots change the way consumers use the internet. An overwhelming majority are also planning to encourage their journalists to behave more like YouTube and TikTok content creators this year, as short-form video and audio content continues to boom. The findings are drawn from a new report from the Reuters Institute for the Study of Journalism, which included the views of 280 media leaders from 51 countries.

Robustness has been extensively studied in reinforcement learning (RL) to handle various forms of uncertainty such as random perturbations, rare events, and malicious attacks. In this work, we consider one critical type of robustness against spurious correlation, where different portions of the state do not have correlations induced by unobserved confounders. These spurious correlations are ubiquitous in real-world tasks, for instance, a self-driving car usually observes heavy traffic in the daytime and light traffic at night due to unobservable human activity. A model that learns such useless or even harmful correlation could catastrophically fail when the confounder in the test case deviates from the training one. Although motivated, enabling robustness against spurious correlation poses significant challenges since the uncertainty set, shaped by the unobserved confounder and causal structure, is difficult to characterize and identify. Existing robust algorithms that assume simple and unstructured uncertainty sets are therefore inadequate to address this challenge. To solve this issue, we propose Robust State-Confounded Markov Decision Processes (RSC-MDPs) and theoretically demonstrate its superiority in avoiding learning spurious correlations compared with other robust RL counterparts. We also design an empirical algorithm to learn the robust optimal policy for RSC-MDPs, which outperforms all baselines in eight realistic self-driving and manipulation tasks.

Contextual linear optimization (CLO) uses predictive contextual features to reduce uncertainty in random cost coefficients and thereby improve average-cost performance. An example is the stochastic shortest path problem with random edge costs (e.g., traffic) and contextual features (e.g., lagged traffic, weather). Existing work on CLO assumes the data has fully observed cost coefficient vectors, but in many applications, we can only see the realized cost of a historical decision, that is, just one projection of the random cost coefficient vector, to which we refer as bandit feedback. We study a class of offline learning algorithms for CLO with bandit feedback, which we term induced empirical risk minimization (IERM), where we fit a predictive model to directly optimize the downstream performance of the policy it induces. We show a fast-rate regret bound for IERM that allows for misspecified model classes and flexible choices of the optimization estimate, and we develop computationally tractable surrogate losses. A byproduct of our theory of independent interest is fast-rate regret bound for IERM with full feedback and misspecified policy class. We compare the performance of different modeling choices numerically using a stochastic shortest path example and provide practical insights from the empirical results.

Tussles between websites and scrapers are not new. Almost since there has been a web to scrape, people have been scraping it and using the data to make search engines, caches and archives, analytics platforms, research datasets, and more. And for almost as long, some websites have objected and tried to stop the scraping with a mix of technical and legal measures. Broadly speaking, scrapers cause two kinds of problems for websites. First, they create bad traffic: millions of automated requests that no human will ever see.

As large AI models become increasingly valuable assets, the risk of model weight exfiltration from inference servers grows accordingly. An attacker controlling an inference server may exfiltrate model weights by hiding them within ordinary model outputs, a strategy known as steganography. This work investigates how to verify model responses to defend against such attacks and, more broadly, to detect anomalous or buggy behavior during inference. We formalize model exfiltration as a security game, propose a verification framework that can provably mitigate steganographic exfiltration, and specify the trust assumptions associated with our scheme. To enable verification, we characterize valid sources of non-determinism in large language model inference and introduce two practical estimators for them. We evaluate our detection framework on several open-weight models ranging from 3B to 30B parameters. On MOE-Qwen-30B, our detector reduces exfiltratable information to <0.5% with false-positive rate of 0.01%, corresponding to a >200x slowdown for adversaries. Overall, this work further establishes a foundation for defending against model weight exfiltration and demonstrates that strong protection can be achieved with minimal additional cost to inference providers.