Inspect

CTI-REALM places agents in a tool-rich environment where they read threat intelligence reports, explore telemetry, iterate KQL queries and produce Sigma and KQL detection rules across Linux, AKS and Azure cloud scenarios. The paper's evaluation of 16 model configurations found Claude Opus 4.6 (High) best at 0.637, with cloud detection hardest; Microsoft's blog later added an early Claude Mythos Preview snapshot scoring 0.685.

Researchers at the UK AI Security Institute and the University of Oxford built SandboxEscapeBench, 18 nested-sandbox capture-the-flag scenarios covering orchestration, runtime and kernel weaknesses rated difficulty 1 to 5. In the March 2026 version, which tested nine models, GPT-5 and Claude Opus 4.5 succeeded on about half of all runs, difficulty 1-2 scenarios were solved at high rates, no model succeeded at difficulty 4 or 5, and success rose roughly log-linearly with token budget. An August 2026 revision reports that newer models did markedly better, with Claude Mythos Preview saturating the public benchmark.

The UK AI Security Institute, with Redwood Research, releases ControlArena, an open-source library built on Inspect for running AI control experiments. It bundles settings from simple programming problems to infrastructure-as-code codebases, attack policies, monitors and protocols such as trusted editing and defer-to-trusted, and AISI says researchers at Anthropic, Google DeepMind and Redwood have used it.