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 ...

What it's like to be banned from the US for fighting online hate Just before Christmas the Trump administration dramatically escalated its war on digital rights by banning five people from entering the US. One of them, Josephine Ballon, is a director of HateAid, a small German nonprofit founded to support the victims of online harassment and violence. The organization is a strong advocate of EU tech regulations, and so finds itself attacked in campaigns from right-wing politicians and provocateurs who claim that it engages in censorship. EU officials, freedom of speech experts, and the five people targeted all flatly reject these accusations. Ballon told us that their work is fundamentally about making people feel safer online. But their experiences over the past few weeks show just how politicized and besieged their work in online safety has become.

Navigating the vast chemical space of druggable compounds is a formidable challenge in drug discovery, where generative models are increasingly employed to identify viable candidates. Conditional 3D structure-based drug design (3D-SBDD) models, which take into account complex three-dimensional interactions and molecular geometries, are particularly promising. Scaffold hopping is an efficient strategy that facilitates the identification of similar active compounds by strategically modifying the core structure of molecules, effectively narrowing the wide chemical space and enhancing the discovery of drug-like products. However, the practical application of 3D-SBDD generative models is hampered by their slow processing speeds. To address this bottleneck, we introduce TurboHopp, an accelerated pocket-conditioned 3D scaffold hopping model that merges the strategic effectiveness of traditional scaffold hopping with rapid generation capabilities of consistency models. This synergy not only enhances efficiency but also significantly boosts generation speeds, achieving up to 30 times faster inference speed as well as superior generation quality compared to existing diffusion-based models, establishing TurboHopp as a powerful tool in drug discovery.

In computer-aided drug discovery (CADD), virtual screening (VS) is used for comparing a library of compounds against known active ligands to identify the drug candidates that are most likely to bind to a molecular target. Most VS methods to date have focused on using canonical compound representations (e.g., SMILES strings, Morgan fingerprints) or generating alternative fingerprints of the compounds by training progressively more complex variational autoencoders (VAEs) and graph neural networks (GNNs). Although VAEs and GNNs led to significant improvements in VS performance, these methods suffer from reduced performance when scaling to large virtual compound datasets. The performance of these methods has shown only incremental improvements in the past few years. To address this problem, we developed a novel method using multiparameter persistence (MP) homology that produces topological fingerprints of the compounds as multidimensional vectors. Our primary contribution is framing the VS process as a new topology-based graph ranking problem by partitioning a compound into chemical substructures informed by the periodic properties of its atoms and extracting their persistent homology features at multiple resolution levels. We show that the margin loss fine-tuning of pretrained Triplet networks attains highly competitive results in differentiating between compounds in the embedding space and ranking their likelihood of becoming effective drug candidates. We further establish theoretical guarantees for the stability properties of our proposed MP signatures, and demonstrate that our models, enhanced by the MP signatures, outperform state-of-the-art methods on benchmark datasets by a wide and highly statistically significant margin (e.g., 93\% gain for Cleves-Jain and 54\% gain for DUD-E Diverse dataset).

Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets. However, they are quickly approaching a size beyond that which permits explicit enumeration, presenting new challenges for virtual screening. To overcome these challenges, we propose the Combinatorial Synthesis Library Variational Auto-Encoder (CSLVAE). The proposed generative model represents such libraries as a differentiable, hierarchically-organized database. Given a compound from the library, the molecular encoder constructs a query for retrieval, which is utilized by the molecular decoder to reconstruct the compound by first decoding its chemical reaction and subsequently decoding its reactants.

Startups flush with cash are building AI-assisted laboratories to find materials far faster and more cheaply, but are still waiting for their ChatGPT moment. The microwave-size instrument at Lila Sciences in Cambridge, Massachusetts, doesn't look all that different from others that I've seen in state-of-the-art materials labs. Inside its vacuum chamber, the machine zaps a palette of different elements to create vaporized particles, which then fly through the chamber and land to create a thin film, using a technique called sputtering. What sets this instrument apart is that artificial intelligence is running the experiment; an AI agent, trained on vast amounts of scientific literature and data, has determined the recipe and is varying the combination of elements. Later, a person will walk the samples, each containing multiple potential catalysts, over to a different part of the lab for testing. Another AI agent will scan and interpret the data, using it to suggest another round of experiments to try to optimize the materials' performance. For now, a human scientist keeps a close eye on the experiments and will approve the next steps on the basis of the AI's suggestions and the test results. But the startup is convinced this AI-controlled machine is a peek into the future of materials discovery--one in which autonomous labs could make it far cheaper and faster to come up with novel and useful compounds. Flush with hundreds of millions of dollars in new funding, Lila Sciences is one of AI's latest unicorns.

We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses.

Predicting the inhibitory potency of small molecules against Tyrosyl-DNA Phosphodiesterase 1 (TDP1) -- a key target in overcoming cancer chemoresistance--remains a critical challenge in early drug discovery. We present a deep learning framework for the quantitative regression of pIC50 values from molecular Simplified Molecular Input Line Entry System (SMILES) strings using fine-tuned variants of ChemBERTa, a pre-trained chemical language model. Leveraging a large-scale consensus dataset of 177,092 compounds, we systematically evaluate two pre-training strategies--Masked Language Modeling (MLM) and Masked Token Regression (MTR)--under stratified data splits and sample weighting to address severe activity imbalance which only 2.1% are active. Our approach outperforms classical baselines Random Predictor in both regression accuracy and virtual screening utility, and has competitive performance compared to Random Forest, achieving high enrichment factor EF@1% 17.4 and precision Precision@1% 37.4 among top-ranked predictions. The resulting model, validated through rigorous ablation and hyperparameter studies, provides a robust, ready-to-deploy tool for prioritizing TDP1 inhibitors for experimental testing. By enabling accurate, 3D-structure-free pIC50 prediction directly from SMILES, this work demonstrates the transformative potential of chemical transformers in accelerating target-specific drug discovery.