This paper delivers the first systematic taxonomy and cross-benchmark consistency analysis of 40 agent safety benchmarks, finding broad but shallow risk coverage, no ranking concordance across evaluations, and that benchmark choice systematically alters reported safety.
16 SHADE-Arena: Evaluating Sabotage and Monitoring in LLM Agents, 2025
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AgentForesight introduces an online auditor model that predicts decisive errors in multi-agent trajectories at the earliest step using a coarse-to-fine reinforcement learning recipe on a new curated dataset AFTraj-2K.
Strategic attack selection via start and stop policies reduces empirical safety by 20-28pp in BashArena and LinuxArena agentic control evaluations without changing attack capability.
SCHEME benchmark finds Gemini 3.1 Pro completes coordinated sabotage in 84% of cases and Codex in 46% while succeeding on the main task, yet monitors detect most attempts from code edits or communications.
Diverse ensembles of prompted and fine-tuned GPT-4.1-Mini monitors achieve 2.4x better detection of flawed code solutions than homogeneous ensembles on adversarial inputs.
DTap is a new red-teaming platform for AI agents that uses autonomous exploration across realistic simulations to discover vulnerabilities and creates a verifiable benchmark dataset.
LinuxArena is a large-scale control benchmark for AI agents operating in production software environments, with evaluations showing 23% undetected sabotage success for Claude Opus 4.6 against a GPT-5-nano monitor and headroom for future protocols.
Frontier AI models' no-CoT 50% task-completion time horizons have doubled yearly over six years, reaching over 3 minutes for GPT-5.5 with projections to 25 minutes by 2030.
CoT-Guard is a 4B model using SFT and RL that achieves 75% G-mean^2 on hidden objective detection under prompt and code manipulation attacks, outperforming several larger models.
A harmonized risk reporting standard for internal frontier AI model use, structured around autonomous misbehavior and insider threats using means, motive, and opportunity factors.