The Department for Science, Innovation and Technology published an independent Lancaster University review that mapped 9,109 peer-reviewed AI-security papers from 2021 through January 2026 across 12 lifecycle themes. Despite rapid publication growth, the review identifies major blind spots in formal verification of training data and model-weight integrity, third-party model provenance, the interaction between AI-specific and conventional IT attack surfaces, end-user and shadow-AI risks, and secure retirement or disposal of frontier models.
The Department for Science, Innovation and Technology published an independent Lancaster University review that mapped 9,109 peer-reviewed AI-security papers from 2021 through January 2026 across 12 lifecycle themes.
Agentic-AI security remains especially immature, including the security of agent tools and inter-agent communication; the report recommends a widely used AI-vulnerability registry analogous to the CVE system.
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GOV.UK
In a randomized field experiment across a chain of middle and high schools in Turkey, giving teachers a generative-AI support tool reduced students’ intrinsic motivation by 0.11 standard deviations. Average academic performance did not change, but students taught by lower-performing teachers experienced significant declines in both performance and confidence, showing that a tool that makes lesson preparation easier for teachers does not automatically improve the student experience.
The European Commission has issued operational guidance for Article 50 of the AI Act before its transparency obligations begin applying on August 2, 2026. Providers must disclose when people are interacting with systems such as chatbots, agents, or avatars and make generative outputs detectable through machine-readable marking; deployers must disclose emotion-recognition or biometric-categorization uses and clearly label deepfakes and certain AI-generated public-interest text when it lacks human review or editorial control.
The NUS/Harbin-led team identifies a domain-specific form of generative-AI hallucination: molecular candidates can receive strong predicted binding scores while violating basic chemistry or producing physically impossible atomic arrangements. Its DrugRPG framework incorporates chemical-foundation-model priors and differentiable physical constraints during molecule generation, reducing severe steric clashes by 65.4% relative to the reported state-of-the-art baseline and increasing by 28.6% the share of generated candidates meeting combined potency, stability, and synthetic-feasibility criteria.