ExfilSquad Targets New Victims, Shares Data via Torrents
Extortion group ExfilSquad lists 13 new US, UK and Swedish victims and now distributes stolen data via peer-to-peer torrents.
ExfilSquad, a data-theft extortion collective that emerged in mid-2026, announced 13 new victims in the US, UK and Sweden with an August 5, 2026 negotiation deadline. The group skips ransomware, instead stealing data and threatening publication on a dark web leak site. Resecurity says its TTPs center on exploiting misconfigured Microsoft Dataverse, Power Pages, case management and CRM portals. The group now distributes stolen data through per-victim torrent trackers and web seeds, making leaks hard to contain.
TLD Tracker: Exploring Newly Released Top
Unit 42 tracked 19 newly released top-level domains and found large-scale phishing, unwanted program distribution, and cybersquatting tied to TLD launch dates.
Researchers analyzed 19 new generic TLDs, including .zip, .bot, .ing, and .meme, released or approaching general availability over roughly 18 months. Data from passive DNS, registry zone files, newly registered domain feeds, and the Tranco top-1M list showed phishing campaigns, potentially unwanted program distribution, and domain squatting on these TLDs. Abuse correlated with each TLD's rollout phases, indicating attackers monitor general availability dates to register and weaponize domains. The IANA root database now lists over 1,000 generic TLDs.
[AINews] OpenAI shuts off Cursor
OpenAI cut off API access to coding tool Cursor after its SpaceX acquisition, citing contract violations by Elon Musk's companies.
OpenAI disabled Cursor's access following the closing of Cursor's acquisition by SpaceX, citing its experience with Elon Musk's companies violating contracts; Cursor responded that OpenAI accounts for only 5% of its traffic. The weekly digest also covers major open-weight releases: Z.ai's GLM-5.3 (744B total/40B active, 1M context) and Tencent's Hy4-preview (770B/49B, ~#5 on Code Arena WebDev), plus Alibaba's Qwen3.8-Flash (125B/6B). vLLM published benchmarks showing no universal winner among speculative decoding methods across model families.