OpenAI announced that it has acquired Promptfoo, a security startup founded in 2024 that protects large language models from adversarial attacks. The deal will integrate Promptfoo’s testing tools into OpenAI Frontier, the company’s enterprise platform for AI agents. Promptfoo, created by Ian Webster and Michael D’Angelo, already serves a significant share of Fortune 500 firms and has raised $23 million. OpenAI said the technology will enable automated red‑teaming, workflow security checks, and risk monitoring for its agentic products, while continuing to support Promptfoo’s open‑source offerings. Read more →

Researchers from Anthropic and ETH Zurich have shown that large language models can link pseudonymous internet profiles to real‑world identities. By analyzing public text for personal clues and matching those clues across the web, the AI system achieved high precision and recall, far outperforming traditional manual methods. The findings raise concerns about the durability of online anonymity for journalists, activists, and everyday users, and suggest that the cost of large‑scale deanonymization could be very low. The authors stress the need for new privacy safeguards as AI capabilities grow. Read more →

In 2026, artificial intelligence can draft emails, summarize meetings, write code, and create caricatures, yet it still falls short in several key areas. Large language models often hallucinate, presenting fabricated facts with confidence. They struggle with simple counting tasks, lack the lived experience needed for therapy, cannot update knowledge in real time, and remain unable to truly understand human nuance. Recognizing these boundaries helps users apply AI tools responsibly and avoid costly mistakes. Read more →

Researchers from ETH Zurich, Anthropic and the Machine Learning Alignment and Theory Scholars program have built an automated AI system that can link pseudonymous online profiles to real identities. Using large language models to analyze writing style, posting patterns and other clues, the system correctly matched up to 68 percent of accounts with 90 percent precision, far outpacing traditional methods. The experiment cost only a few dollars per profile, highlighting a low‑cost barrier for large‑scale deanonymization. The study warns that online anonymity may be less secure than many assume, especially as AI capabilities continue to improve. Read more →

Researchers demonstrated that large language model (LLM) agents can extract identity clues from free‑text data, search the web autonomously, and match those clues to real‑world individuals. In experiments using interview transcripts, Reddit comments, and a large pool of Reddit users, the AI was able to correctly re‑identify a measurable share of participants while maintaining high precision. The findings highlight a growing capability of AI to breach pseudonymity, raising concerns about privacy in online platforms. Read more →