The use of cryptocurrency in sales of human beings for prostitution and scam compounds nearly doubled in 2025, according to a conservative estimate. Many of the deals are happening in plain sight. Cryptocurrency's frictionless, transnational, low-regulation transactions have long promised the ability to pay anyone in the world for anything. More than ever before, that anything includes human beings: victims of human trafficking forced into scam compounds and the sex trade on an industrial scale, bought and sold in crypto deals carried out with impunity, often in full public view. In new research published today, crypto-tracing firm Chainalysis found that crypto-funded transactions for human trafficking--largely forced laborers trapped in compounds across Southeast Asia and coerced into working as online scammers, as well as sex-trafficking prostitution rings--grew explosively in 2025.

A 2025 survey in the forests of Laos, Vietnam, and Cambodia uncovered several rare and endangered animals. A pig-tailed macaque is caught on camera in a Cambodian forest. Breakthroughs, discoveries, and DIY tips sent six days a week. The results of a new camera-trap survey in Southeast Asia is revealing a bevy of hidden biodiversity tucked within the Annamites mountain range . This largely unexplored wildlife hotspot has a forest stretching 683 miles (1,100 kilometers) across the countries of Laos, Vietnam, and Cambodia.

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Russian overnight drone attacks on Ukraine, including in the eastern Kharkiv and Chernihiv regions, killed at least four people. A mother and her 10-year-old son were killed in the attacks, which also knocked out power to tens of thousands of people, Ukrainian officials said.

Security News This Week: Jeffrey Epstein Had a'Personal Hacker,' Informant Claims Plus: AI agent OpenClaw gives cybersecurity experts the willies, China executes 11 scam compound bosses, a $40 million crypto theft has an unexpected alleged culprit, and more. As the standoff between the United States government and Minnesota continues this week over immigration enforcement operations that have essentially occupied the Twin Cities and other parts of the state, a federal judge delayed a decision this week and ordered a new briefing on whether the Department of Homeland Security is using armed raids to pressure Minnesota into abandoning its sanctuary policies for immigrants. Meanwhile, minutes after a federal immigration officer shot and killed 37-year-old Alex Pretti in Minneapolis last Saturday, Trump administration officials and right-wing influencers had already mounted a smear campaign, calling Pretti a "terrorist" and a "lunatic ." As part of its surveillance dragnet, Immigration and Customs Enforcement has been using an AI-powered Palantir system since last spring to summarize tips sent to its tip line, according to a newly released Homeland Security document. DHS immigration agents have also been using the now notorious face recognition app Mobile Fortify to scan the faces of countless people in the US--including many citizens .

A whistleblower trapped inside a "pig butchering" scam compound gave WIRED a vast trove of its internal materials--including 4,200 pages of messages that lay out its operations in unprecedented detail. Just before 8am one day last April, an office manager who went by the name Amani sent out a motivational message to his colleagues and subordinates. "Every day brings a new opportunity--a chance to connect, to inspire, and to make a difference," he wrote in his 500-word post to an office-wide WhatsApp group. "Talk to that next customer like you're bringing them something valuable--because you are." He and his underlings worked inside a " pig butchering " compound, a criminal operation built to carry out scams --promising romance and riches from crypto investments--that often defraud victims out of hundreds of thousands or even millions of dollars at a time. The workers Amani was addressing were eight hours into their 15-hour night shift in a high-rise building in the Golden Triangle special economic zone in Northern Laos. Like their marks, most of them were victims, too: forced laborers trapped in the compound, held in debt bondage with no passports. They struggled to meet scam revenue quotas to avoid fines that deepened their debt.

A source trapped inside an industrial-scale scamming operation contacted me, determined to expose his captors' crimes--and then escape. It was a perfect June evening in New York when I received my first email from the source who would ask me to call him Red Bull. He was writing from hell, 8,000 miles away. A summer shower had left a rainbow over my Brooklyn neighborhood, and my two children were playing in a kiddie pool on the roof of our apartment building. Now the sun was setting, while I--in typical 21st-century parenting fashion, forgive me--compulsively scrolled through every app on my phone. The message had no subject line and came from an address on the encrypted email service Proton Mail: "vaultwhistle@proton.me." I'm currently working inside a major crypto romance scam operation based in the Golden Triangle," it began. "I am a computer engineer being forced to work here under a contract." "I've collected internal evidence of how the scam works--step by step," the message ...

Adapting large-scale pretrained language models to downstream tasks via fine-tuning is the standard method for achieving state-of-the-art performance on NLP benchmarks.

As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior work has primarily examined socio-demographic and left--right political dimensions, little attention has been paid to how LLMs align with broader geopolitical value systems, particularly the democracy--authoritarianism spectrum. In this paper, we propose a novel methodology to assess such alignment, combining (1) the F-scale, a psychometric tool for measuring authoritarian tendencies, (2) FavScore, a newly introduced metric for evaluating model favorability toward world leaders, and (3) role-model probing to assess which figures are cited as general role-models by LLMs. We find that LLMs generally favor democratic values and leaders, but exhibit increased favorability toward authoritarian figures when prompted in Mandarin. Further, models are found to often cite authoritarian figures as role models, even outside explicit political contexts. These results shed light on ways LLMs may reflect and potentially reinforce global political ideologies, highlighting the importance of evaluating bias beyond conventional socio-political axes. Our code is available at: https://github.com/irenestrauss/Democratic-Authoritarian-Bias-LLMs.

We propose a complement to constitutive modeling that augments neural networks with material principles to capture anisotropy and inelasticity at finite strains. The key element is a dual potential that governs dissipation, consistently incorporates anisotropy, and-unlike conventional convex formulations-satisfies the dissipation inequality without requiring convexity. Our neural network architecture employs invariant-based input representations in terms of mixed elastic, inelastic and structural tensors. It adapts Input Convex Neural Networks, and introduces Input Monotonic Neural Networks to broaden the admissible potential class. To bypass exponential-map time integration in the finite strain regime and stabilize the training of inelastic materials, we employ recurrent Liquid Neural Networks. The approach is evaluated at both material point and structural scales. We benchmark against recurrent models without physical constraints and validate predictions of deformation and reaction forces for unseen boundary value problems. In all cases, the method delivers accurate and stable performance beyond the training regime. The neural network and finite element implementations are available as open-source and are accessible to the public via https://doi.org/10.5281/zenodo.17199965.

Safeguard models help large language models (LLMs) detect and block harmful content, but most evaluations remain English-centric and overlook linguistic and cultural diversity. Existing multilingual safety benchmarks often rely on machine-translated English data, which fails to capture nuances in low-resource languages. Southeast Asian (SEA) languages are underrepresented despite the region's linguistic diversity and unique safety concerns, from culturally sensitive political speech to region-specific misinformation. Addressing these gaps requires benchmarks that are natively authored to reflect local norms and harm scenarios. We introduce SEA-SafeguardBench, the first human-verified safety benchmark for SEA, covering eight languages, 21,640 samples, across three subsets: general, in-the-wild, and content generation. The experimental results from our benchmark demonstrate that even state-of-the-art LLMs and guardrails are challenged by SEA cultural and harm scenarios and underperform when compared to English texts.