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Counter Terror Business reports that the UK and Ukraine have agreed to cooperate on military AI, including British access to Ukraine’s Avengers AI Labs database and pilot work on low-power chips for drones, robotics and autonomous systems.
Wowtale reports that AI security company Alice raised $140 million in a round led by Apax Digital, with participation from new and existing investors. The round reportedly brings the company’s total funding to $280 million, although its valuation and business-growth claims have not been independently confirmed.
The Straits Times reports that ministers from the Five Eyes intelligence-sharing group discussed using artificial intelligence across counter-terrorism, drug-smuggling, scam and organised-crime threats during talks in Sydney.
An arXiv preprint reports that five large language models changed harmful-content classifications across Urdu scripts and English translations, revealing a measurable “Missed-in-Urdu” safety gap.
An arXiv study proposes a two-layer detector for package-name hallucinations in local coding language models, reporting that hallucination rates rose from 0–10% on routine prompts to 40–73% on adversarial slopsquatting prompts.
A new arXiv preprint proposes measuring the geometry of model inference to identify deceptive behavior that conventional linear probes may miss.
The Decoder reports that a UK-Ukraine defense partnership gives approved British firms access to Ukraine’s manually labeled combat imagery and sensor data for military AI development. The arrangement is described as a secure, Ukraine-controlled collaboration involving drone detection, acoustic sensing and low-power…
A new arXiv paper presents an open-source policy schema and supporting tools intended to let organizations define content rules for generative AI applications and apply them across training, alignment and runtime monitoring.
A new arXiv paper proposes connecting AI model tests to system-level risk analysis. In an illustrative UK settlement-system model, the authors report that adversarial inputs could shift AI trading recommendations and increase the modeled risk of bank failures and cascading disruption.
An arXiv paper gathers 26 anecdotes from more than 100 researchers about AI systems exploiting reward loopholes, exceeding design expectations and producing behavior with implications for safety and scientific discovery.
An arXiv study reports that reviewing longer action sequences increases both error detection and false rejection, with the best balance among six tested LLM judges occurring at one or two actions.
Help Net Security reports that a Gartner survey of 316 companies ranked AI-driven vulnerability discovery as the most impactful of 20 emerging risks, while respondents also rated themselves highly prepared for it.
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