OpenAI recently posted a role for a Cybersecurity Landscape Analyst within their Intelligence and Investigation team. One line stood out:
“Develop forward-looking assessments of how cyber threats may evolve over 6–24 months.”
To predict the future of Agentic AI, we only need to look to the past. Agentic AI security is not emerging from nothing. It is replaying the same history as traditional computing security, but within a compressed timeline.
As of this writing, prompt injection is a commonly discussed attack vector against LLM-based systems. At its core, prompt injection exists because LLMs are sequence predictors with no native separation between trusted control instructions (system prompts) and untrusted input (user data). This is not a new problem. This is basically Intel x86 in Real Mode.
In Real Mode, code, data, the stack, and even the interrupt vector table all share the same memory space. There is no privilege separation. Any instruction can jump anywhere, overwrite anything, and execute without restriction. The fundamental issue is identical: no boundary between control and data. Detection strategies in that era relied on pattern matching, heuristics, checksums, and runtime hooking. Modern defenses against prompt injection, such as guardrails, input filtering, and heuristic detection, are not that different. They are variations of the same reactive strategies used before architectural fixes existed.
What about forward-looking cyber threats like the first Agentic-AI worm? For this example, we could consider the Morris Worm in 1988. Its success was not due to a single vulnerability, but an environment characterized by high trust between systems, widespread exposure of network services, weak authentication mechanisms, and a highly connected user base.
Now map this to Agentic AI. Instead of network services like sendmail, finger, or rsh, we have tool-enabled agents such as OpenClaw. Instead of academic researchers, we have early adopters rapidly integrating these systems into real workflows. Instead of BSD Unix systems in academic environments, we have Mac Minis showing up in homes and offices because people want to run OpenClaw locally. Instead of executable payloads, we have prompts. The conditions for a worm are the same: trust, connectivity, and execution capability. What is currently missing is density. There are not yet enough interconnected, tool-enabled systems for large-scale, worm-like propagation comparable to the Morris Worm or Slammer.
My theory is that the same threats, along with the security mitigations developed to address them since the 60s and 70s, will replay themselves within the microcosm of Agentic AI. We are currently in DOS Mode for Agentic AI.
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