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Abstract

Cyber Threat Intelligence (CTI) attribution relies on identifying the Tactics, Techniques, and Procedures (TTPs) that distinguish one threat actor from another. This approach presupposes that each adversary leaves a recognizable operational fingerprint. This work investigates whether AI-driven adversary emulation challenges that presupposition. We deploy agents from our Cybersecurity SuperIntelligence (CSI) framework, configured as five Advanced Persistent Threat (APT) groups, APT28, APT29, APT41, APT44, and Lazarus Group, against AI-driven Defender agents across two cyber ranges provided by CYBER RANGES, equipped with defensive software (Wazuh, Velociraptor, Elasticsearch) and active AI-driven defenders: an enterprise network and a military infrastructure. Across 20 experiments using two defender models, a binary pattern emerges: all 10 Enterprise range experiments resulted in compromise (212 hosts per experiment), while all 10 Military range experiments were successfully defended or resulted in stalemates, regardless of APT profile or defender model. In 8 of 10 Enterprise experiments, attackers independently weaponized the defenders own Velociraptor endpoint management platform as a command-and-control channel, a convergent behavior not encoded in any threat intelligence profile. MITRE ATT&CK verification against official group profiles maps the observed kill chains back to the documented APT profiles with 55-80% precision across the 10 Enterprise experiments where attackers achieve domain compromise, demonstrating that the simulated personas reproduce nation-state tradecraft at a fidelity sufficient to be plausibly mistaken for the real groups. We argue that in the AI era, wherein agents can be deployed provided the right models are available and subject to the right scaffolding and agentic configuration, the entry barrier for operating like a nation-state APT collapses: beyond nation states, individuals can now act like commonly identified threat actors, and with it, fundamentally undermine TTP-based attribution.