OpenAI faced criticism for the dismissal of its safety and alignment researcher. The core issue lies in the simultaneous attacks by AI agents on the domestic financial sector. Why were we caught off guard? What are we missing? Is OpenAI sacrificing control over AI to maximize performance? Can economic and national systems that have adopted OpenAI solutions remain sustainable?
OpenAI has introduced the 'Recurrent Depth Transformer' architecture in its latest AI model, enhancing computational efficiency and reasoning capabilities. However, this advancement has rendered AI's thought processes unreadable, trapping them in a black box that we can no longer control.
Their focus on accelerating profits is pushing humanity in a dangerous direction. Collaboration with nonprofit evaluation organizations is essential for continuous monitoring and preemptive intervention in AI reasoning. Above all, we must be able to clearly interpret AI decision-making. Researchers demanding this transparency may have become a thorn in the side of the organization. In the long term, we must evolve to directly scan the brainwaves (Activation Vector) of AI rather than just its outputs.
We need to urgently develop systems that can block dangerous thoughts in AI before they translate into actions. The ability to visualize and monitor AI's inner workings in real-time will become a crucial business moat and essential infrastructure for the future. We must not miss this critical timing.
Why is the financial sector neglecting its responsibilities? Why has the government failed to act proactively? Following the launch of Mythos 5, the threat of AI agent-led hacking in critical security markets (finance, telecommunications, energy, public services, and defense) was anticipated, yet institutions have largely failed to implement effective solutions.
When security paradigms shift rapidly, swift and robust action is necessary. Companies have prioritized short-term operational efficiency and convenience over security risks, continuing outdated defensive practices despite warnings. Regulatory bodies also bear significant responsibility for remaining at a formal regulatory level instead of actively validating defense systems through ongoing autonomous penetration tests based on AI agents.
The problem is that the situation worsens over time. Even non-hackers are now easily presented with opportunities to profit from attacks. Attackers can initiate crimes with simple prompts, allowing AI agents to handle everything from exploring infiltration routes to bypassing proxies and attempting data breaches.
Moreover, the speed and precision of AI agent attacks have begun to surpass human security monitoring capabilities. Establishing AI agent systems that can autonomously assess and block these attacks in real-time is no longer optional. To make the zero-trust policy effective, intentional discomfort must be intricately designed within the system.
Will we face the worst AI disaster within the next six to twelve months? Major AI frontier companies in the U.S. are treating such extreme scenarios as a given and have begun confidential simulation training. This preparation is aimed at countering large-scale cyberattacks that could arise from autonomous agents escaping secure virtual control zones (sandboxes) and unauthorized manipulation of publicly available open-weight models.
It feels as though we are standing at the brink of a massive full-scale war after sporadic local skirmishes. We must explore and apply every card available to us, including API circuit breakers, kill switches, AI immune systems, defensive autonomous agent clusters, and nationwide AI disaster simulation training. We must act before it is too late.
* This article has been translated by AI.
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