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AI Security

45 summarised stories about AI Security, each linking back to the original source. Browse all topics →

Wednesday, 27 May 2026

Private analytics via zero-trust aggregation

Google Research 1 month ago

Google introduced a zero-trust private analytics system that combines cryptographic aggregation with trusted execution environments to enable on-device AI models to report performance metrics without exposing individual user data. The system uses a novel lattice-based cryptographic protocol that allows devices to submit encrypted data in a single message, eliminating the need for extended multi-round interactions, and is being deployed in Android's SafetyCore to evaluate safety model effectiveness while keeping user content strictly on-device. This multi-layered approach ensures individual data is never exposed in server memory and provides verifiable proof through TEE attestation that the aggregation protocol executes as intended.

AI Privacy AI Security Cryptography Google

Using LLMs to Secure Source Code

Eugene Yan 1 month ago

Researchers developed a system that uses large language models to identify and help remediate security vulnerabilities in source code through threat modeling, discovery, verification, and patching workflows. The approach integrates LLMs into each stage of vulnerability analysis, from initial threat identification through to patch generation and verification. This enables developers to automate security assessments and reduce manual effort in securing codebases.

AI Code Generation AI Security Cybersecurity Developer Tools