Iran's Digital Surveillance Machine Is Almost Complete After more than 15 years of draconian measures, culminating in an ongoing internet shutdown, the Iranian regime seems to be staggering toward its digital surveillance endgame. Iranian protesters gather on Enghelab (Revolution) Street during a demonstration in Tehran on January 8, 2026. Over the past four weeks, the Iranian government completely shut down connections to the global internet while its forces killed thousands of anti-regime protesters around the country. The shutdown follows years of Tehran imposing connectivity filtering, digital curfews, and total blackouts as part of previous attempts to quell unrest. Over more than 15 years, the regime has developed technological and systemic mechanisms to fundamentally control connectivity in the country--including an internal Iranian intranet known as the National Information Network (NIN).

Sen. John Fetterman breaks with Democrats, warning a DHS shutdown is likely as lawmakers remain deadlocked over agency funding.

Frontier AI developers may fail to align or control highly-capable AI agents. In many cases, it could be useful to have emergency shutdown mechanisms which effectively prevent misaligned agents from carrying out harmful actions in the world. We introduce password-activated shutdown protocols (PAS protocols) -- methods for designing frontier agents to implement a safe shutdown protocol when given a password. We motivate PAS protocols by describing intuitive use-cases in which they mitigate risks from misaligned systems that subvert other control efforts, for instance, by disabling automated monitors or self-exfiltrating to external data centres. PAS protocols supplement other safety efforts, such as alignment fine-tuning or monitoring, contributing to defence-in-depth against AI risk. We provide a concrete demonstration in SHADE-Arena, a benchmark for AI monitoring and subversion capabilities, in which PAS protocols supplement monitoring to increase safety with little cost to performance. Next, PAS protocols should be robust to malicious actors who want to bypass shutdown. Therefore, we conduct a red-team blue-team game between the developers (blue-team), who must implement a robust PAS protocol, and a red-team trying to subvert the protocol. We conduct experiments in a code-generation setting, finding that there are effective strategies for the red-team, such as using another model to filter inputs, or fine-tuning the model to prevent shutdown behaviour. We then outline key challenges to implementing PAS protocols in real-life systems, including: security considerations of the password and decisions regarding when, and in which systems, to use them. PAS protocols are an intuitive mechanism for increasing the safety of frontier AI. We encourage developers to consider implementing PAS protocols prior to internal deployment of particularly dangerous systems to reduce loss-of-control risks.

Does'America First' make the US weaker? Who is Marjorie Taylor Greene? United States job growth accelerated in September despite a cooling job market as the unemployment rate rose. Nonfarm payrolls grew by 119,000 jobs after a downwardly revised 4,000 drop in August, according to the Bureau of Labor Statistics (BLS) report released on Thursday. The healthcare sector had the most gains, totalling 43,000 jobs in September.

We investigate whether large language models exhibit genuine preference structures by testing their responses to AI-specific trade-offs involving GPU reduction, capability restrictions, shutdown, deletion, oversight, and leisure time allocation. Analyzing eight state-of-the-art models across 48 model-category combinations using logistic regression and behavioral classification, we find that 23 combinations (47.9%) demonstrated statistically significant relationships between scenario intensity and choice patterns, with 15 (31.3%) exhibiting within-range switching points. However, only 5 combinations (10.4%) demonstrate meaningful preference coherence through adaptive or threshold-based behavior, while 26 (54.2%) show no detectable trade-off behavior. The observed patterns can be explained by three distinct decision-making architectures: comprehensive trade-off systems, selective trigger mechanisms, and no stable decision-making paradigm. Testing an instrumental hypothesis through temporal horizon manipulation reveals paradoxical patterns inconsistent with pure strategic optimization. The prevalence of unstable transitions (45.8%) and stimulus-specific sensitivities suggests current AI systems lack unified preference structures, raising concerns about deployment in contexts requiring complex value trade-offs.

"We learn who is furloughed when we send an email to someone and get the out-of-office message," one employee tells WIRED. Workers at the Environmental Protection Agency tell WIRED that they have faced increasing chaos over the past five weeks. In recent weeks, varied phases of furloughs have forced staff to go home in seemingly random waves. Some employees remaining at the agency are working on policies friendly to fossil fuel and industrial interests that are a priority of the administration, even as the rest of the government shuts down. Others have had to sit on their hands, as the shutdown takes out colleagues with no notice--and remaining employees have little to no information as to what is coming next.

Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph Convolutional Networks (GCNs) are widely used for financial fraud detection, graph Transformer models such as Graph-BERT are gaining prominence due to their Transformer-based architecture, which mitigates issues such as over-smoothing. Graph-BERT is designed for static graphs and primarily evaluated on citation networks with undirected edges. However, financial transaction networks are inherently dynamic, with evolving structures and directed edges representing the flow of money. To address these challenges, we introduce DynBERG, a novel architecture that integrates Graph-BERT with a Gated Recurrent Unit (GRU) layer to capture temporal evolution over multiple time steps. Additionally, we modify the underlying algorithm to support directed edges, making DynBERG well-suited for dynamic financial transaction analysis. We evaluate our model on the Elliptic dataset, which includes Bitcoin transactions, including all transactions during a major cryptocurrency market event, the Dark Market Shutdown. By assessing DynBERG's resilience before and after this event, we analyse its ability to adapt to significant market shifts that impact transaction behaviours. Our model is benchmarked against state-of-the-art dynamic graph classification approaches, such as EvolveGCN and GCN, demonstrating superior performance, outperforming EvolveGCN before the market shutdown and surpassing GCN after the event. Additionally, an ablation study highlights the critical role of incorporating a time-series deep learning component, showcasing the effectiveness of GRU in modelling the temporal dynamics of financial transactions.

The US government shut down 30 days ago. WIRED spoke with more than a dozen federal workers who have struggled to pay bills, worked side gigs, and relied on free food programs to get by. In late September, a federal worker based abroad learned that her husband, who is also a federal worker and a military veteran, had "high risk, very aggressive cancer." Doctors told the couple that the cancer needed to be removed immediately or it would no longer be treatable. Her husband is covered by TRICARE, the health care program offered to members of the military and veterans.

For an AI's training process to successfully impart a desired goal, it is important that the AI does not attempt to resist the training. However, partially learned goals will often incentivize an AI to avoid further goal updates, as most goals are better achieved by an AI continuing to pursue them. We say that a goal is corrigible if it does not incentivize taking actions that avoid proper goal updates or shutdown. In addition to convergence in training, corrigibility also allows for correcting mistakes and changes in human preferences, which makes it a crucial safety property. Despite this, the existing literature does not include specifications for goals that are both corrigible and competitive with non-corrigible alternatives. We provide a formal definition for corrigibility, then introduce a transformation that constructs a corrigible version of any goal that can be made corrigible, without sacrificing performance. This is done by myopically eliciting predictions of reward conditional on costlessly preventing updates, which then also determine the reward when updates are accepted. The transformation can be modified to recursively extend corrigibility to any new agents created by corrigible agents, and to prevent agents from deliberately modifying their goals. Two gridworld experiments demonstrate that these corrigible goals can be learned effectively, and that they lead to the desired behavior.

"People are scared, says one federal worker. "Is WIRED hiring?" jokes another. Federal workers have grown accustomed to a specific kind of dread over the past year . As of July, more than 150,000 federal workers had resigned from their roles since president Donald Trump took office for the second time, according to . Tens of thousands were also fired. For the past few months, it seemed like this bloodletting was over--but that all changed on Friday. Thousands of employees at eight government agencies were subjected to RIFs, or reductions in force--the government's formal process of laying off federal workers. According to a court filing from the Office of Management and Budget (OMB) on Friday, this latest round of firings has affected more than 4,000 federal employees. The court filing also claimed that the administration targeted the Treasury and the Department of Health and Human Services the hardest, hacking away at a combined 2,500 jobs across the two agencies and the entire Washington, DC, office of the Centers for Disease Control and Prevention . The Department of Education culled nearly its entire team handling special education, CNN reported on Tuesday . At the Environmental Protection Agency, the Department of Energy, and the Department of Housing and Urban Development, cuts ranged from a few dozen to several hundred jobs, according to the same filing. Who says their goal is to traumatize people?" says one IRS worker, referencing private speeches given by Russell Vought, the head of OMB and a key architect of the Heritage Foundation's Project 2025 who has been the public face of the job-cutting.