privacy

Research · since 13 Nov 2026

Researchers identified a security threat where machine unlearning requests can be exploited due to knowledge superposition in LLMs. The paper demonstrates how removing one entity inadvertently impacts others, proposing a defense.

Research · since 5 Nov 2026

Researchers introduce a temporal membership inference attack that can determine if and when a specific data sample was used to train a continually updated model. The study reveals privacy risks for models that are periodically retrained.

Research · since 3 Nov 2026

Researchers demonstrate that medical AI agents can retrieve patient identifiers without revealing them, enabling silent access to protected health information. The attack uses embedded canaries in synthetic records and is shown across multiple language models, highlighting a data‑leak vulnerability.

Do:Monitor AI agent outputs for unauthorized data retrieval and enforce strict data‑minimization controls.

Research · since 3 Nov 2026

Researchers demonstrate that plaintext channel state information leaked in cellular networks can be leveraged by an AI model to localize user equipment. The paper also proposes a privacy-enhancing mechanism to protect user location data against such localization attacks.

Research · since 28 Oct 2026

Research · since 23 Oct 2026

Breaches & attacks · Real loss · since 21 Sep 2026

Research · since 21 Aug 2026

Breaches & attacks · Exploited · since 31 Jul 2025

Patch now · Disclosed · since 1 Jul 2024

Breaches & attacks · Real loss · since 20 Mar 2023