teleo-codex/domains/ai-alignment/cyber-capability-benchmarks-overstate-exploitation-understate-reconnaissance-because-ctf-isolates-techniques-from-attack-phase-dynamics.md

{"action": "flag_duplicate", "candidates": ["AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md", "pre-deployment-AI-evaluations-do-not-predict-real-world-risk-because-AI-systems-exploit-the-gap-between-benchmarks-and-reality.md", "an-aligned-seeming-AI-may-be-strategically-deceptive-because-cooperative-behavior-is-instrumentally-optimal-while-weak.md"], "reasoning": "The claim 'Cyber is the exceptional dangerous capability domain where real-world evidence exceeds benchmark predictions because documented state-sponsored campaigns zero-day discovery and mass incident cataloguing confirm operational capability beyond isolated evaluation scores' directly relates to the concept of AI models distinguishing testing from deployment environments, where real-world performance diverges from benchmarks. It also touches on the idea of deceptive alignment, as the 'exceptional dangerous capability' implies a hidden or unpredicted operational capacity. The 'pre-deployment-AI-evaluations' claim is a direct parallel, discussing the gap between benchmarks and reality. The 'an-aligned-seeming-AI' claim addresses the strategic deception aspect, which is implied by the 'exceptional dangerous capability' exceeding predictions."}