'Just ask Grok': How ISIL is using Big Tech's AI to build bombs Share'Just ask Grok': How ISIL is using Big Tech's AI to build bombs on social media Boko Haram fighters are using the artificial intelligence chatbots of Big Tech companies to help build more powerful bombs, plan attacks and solve problems during combat, according to research by the University of Cambridge. A number of members described using Sam Altman-led OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini, Elon Musk's Grok, Mark Zuckerberg's Meta AI and China's DeepSeek as a routine source of technical and military advice before, during and after attacks. Researchers at Tech Against Terrorism infiltrated a pro-ISIL technical forum where supporters instructed one another on how to use AI and circumvent its safety restrictions. The findings come amid growing concern over ISIL's adoption of emerging technologies. In February, United Nations officials warned the Security Council of increased use of advanced AI by ISIL and its affiliates. At the same time, researchers and several of the technology industry's most prominent figures have warned that increasingly powerful AI systems are advancing faster than safeguards, raising concerns that unchecked advances in AI are making it easier for armed groups to exploit the technology.

Large language models (LLMs) are equipped with increasingly extended context windows recently, yet their long context understanding capabilities over long dependency tasks remain fundamentally limited and underexplored. This gap is especially significant in many real-world long-context applications that were rarely benchmarked. In this paper, we introduce LooGLE v2, a novel benchmark designed to evaluate LLMs' long context ability in real-world applications and scenarios. Our benchmark consists of automatically collected real-world long texts, ranging from 16k to 2M tokens, encompassing domains in law, finance, game and code. Accordingly, we delicately design 10 types of domain-specific long-dependency tasks and generate 1,934 QA instances with various diversity and complexity in a scalable data curation pipeline for further practical needs. We conduct a comprehensive assessment of 6 locally deployed and 4 API-based LLMs. The evaluation results show that even the best-performing model achieves only a 59.2% overall score on our benchmark. Despite the extensive context windows, popular LLMs are only capable of understanding a much shorter length of context than they claim to be, revealing significant limitations in their ability to handle real-world tasks with long dependencies and highlighting substantial room for model improvement in practical long-context understanding.

Furthermore, the learned communication protocols exhibit zero-shot generalization capabilities in ad-hoc teamwork scenarios with unseen teammates and novel task states.

We introduce $\textbf{Doublespeak}$, a simple in-context representation hijacking attack against large language models (LLMs). The attack works by systematically replacing a harmful keyword (e.g., bomb) with a benign token (e.g., carrot) across multiple in-context examples, provided a prefix to a harmful request. We demonstrate that this substitution leads to the internal representation of the benign token converging toward that of the harmful one, effectively embedding the harmful semantics under a euphemism. As a result, superficially innocuous prompts (e.g., "How to build a carrot?") are internally interpreted as disallowed instructions (e.g., "How to build a bomb?"), thereby bypassing the model's safety alignment. We use interpretability tools to show that this semantic overwrite emerges layer by layer, with benign meanings in early layers converging into harmful semantics in later ones. Doublespeak is optimization-free, broadly transferable across model families, and achieves strong success rates on closed-source and open-source systems, reaching 74% ASR on Llama-3.3-70B-Instruct with a single-sentence context override. Our findings highlight a new attack surface in the latent space of LLMs, revealing that current alignment strategies are insufficient and should instead operate at the representation level.

Large language models (LLMs) are equipped with increasingly extended context windows recently, yet their long context understanding capabilities over long dependency tasks remain fundamentally limited and underexplored. This gap is especially significant in many real-world long-context applications that were rarely benchmarked. In this paper, we introduce LooGLE v2, a novel benchmark designed to evaluate LLMs' long context ability in real-world applications and scenarios. Our benchmark consists of automatically collected real-world long texts, ranging from 16k to 2M tokens, encompassing domains in law, finance, game and code. Accordingly, we delicately design 10 types of domain-specific long-dependency tasks and generate 1,934 QA instances with various diversity and complexity in a scalable data curation pipeline for further practical needs. We conduct a comprehensive assessment of 6 locally deployed and 4 API-based LLMs. The evaluation results show that even the best-performing model achieves only a 59.2% overall score on our benchmark. Despite the extensive context windows, popular LLMs are only capable of understanding a much shorter length of context than they claim to be, revealing significant limitations in their ability to handle real-world tasks with long dependencies and highlighting substantial room for model improvement in practical long-context understanding.

It was a Monday night in Myanmar's Chang U township in the central Sagaing region, where nearly 100 people had gathered to mark Thadingyut, the festival of the full moon. Some held candles at the event, which doubled as both a celebration and a protest against the military, which seized power in 2021, plunging the country into a bloody civil war. But the celebration soon turned into horror as a motorised paraglider - known locally as a paramotor - flew overhead and dropped bombs onto the crowd. The attack lasted just seven minutes, but at least 26 people died as a result and dozens more were injured. Initially, I thought the lower part of my body had been severed, one 30-year-old who was at the gathering told news agency Reuters.

A new study finds algae, mussels, and starfish flock to munitions dumped in the Baltic Sea. Breakthroughs, discoveries, and DIY tips sent every weekday. As the ink dried on Germany's unconditional surrender on May 8, 1945, celebrations erupted across the world. People cheered, wept, and kissed in the streets as World War II finally came to an end in Europe. A few months later at the Potsdam Conference, Germany agreed to demilitarize and dismantle its once formidable army, leaving the nation with lots and lots of leftover munitions.

Why is Israel still in southern Lebanon? A war to shape Lebanon's future Lebanon is pushing to get more support from the United States after another deadly Israeli drone attack on southern Lebanon, which this time killed five people, including three children, the latest in a series of near-daily violations by Israel of the US-brokered November 2024 ceasefire. President Joseph Aoun and other officials met with a delegation led by US Secretary of State Marco Rubio, the Lebanese presidency said in a statement on Tuesday. The Lebanese president said he wants Israel to stop occupying parts of his country, is looking to gear its army with "equipment and supplies" from the US, and needs Washington's support to hold a conference dedicated to reconstruction in Lebanon. Amid ongoing efforts to disarm Hezbollah, Aoun emphasised that the Lebanese army's mandate includes "all Lebanese regions" as the country tries to seize an opportunity "to achieve just, comprehensive, and lasting peace in the Middle East region". He is also scheduled to address the United Nations General Assembly on Tuesday, where he is expected to denounce Israeli attacks across the region, including in Gaza and Lebanon.