At the start of 2026, we are already witnessing a major shift from reactive or Generative AI to sophisticated Agentic AI. This is fundamentally changing the risk landscape for Critical National Infrastructure (CNI) and domains where mission-critical software is essential. Unlike static software, agentic systems possess agency, with growing ability to reason, use tools, and execute multi-step plans without a human in the loop. In sectors like power grids, water treatment, and healthcare, this autonomy creates a disturbing new vector for systemic poisoning.
The most insidious threat today is long-term memory poisoning. While traditional cyberattacks are typically overt—designed to trigger immediate, visible failures—Agentic AI has enabled a new era of “slow-burn” compromises.
These exploits capitalise on the very feature that makes agents powerful, which is their ability to retain context over time. Because these systems continuously reference a persistent knowledge base to inform their reasoning, an attacker can subtly salt that memory with malicious data. Instead of causing an instantaneous system crash, this injection warps the agent’s future logic. It effectively turns a trusted autonomous partner into a compromised insider that executes flawed or dangerous actions based on a poisoned reality, often remaining undetected until the damage is irreversible.
Imagine the following scenario: a maintenance agent for a regional power grid might be fed poisoned documentation suggesting that a specific voltage fluctuation is a standard sensor error rather than a precursor to a transformer fire. The agent incorporates this as a truth, effectively becoming a sleeper agent that will ignore critical safety triggers months later. This is not a code exploit; it is a cognitive exploit.
As we know, CNI and mission-critical systems are rarely isolated. This is the perfect breeding ground for Agentic AI to operate in swarms or multi-agent workflows. This creates a cascading failure risk that operates at machine speed and effectively presents the industry with chained vulnerabilities.
The cascading effect here is that it creates confusion where a low-privilege scheduling agent might be tricked via an Indirect Prompt Injection (IPJ), such as a malicious email or calendar invite, into requesting an emergency shut-off from a high-privilege infrastructure agent. Or, it cascades into Cross-Agent Escalation. This is not difficult to initiate because agents often trust peer agents; this means a compromise in a non-critical system, like a building’s HVAC, AI can be used to pivot into a safety-critical system, like a hospital’s oxygen delivery by spoofing legitimate agent-to-agent requests.
The problem is that while organisations focus on hallucinations and bias, they are largely ignoring three structural realities:
- Developers frequently grant agents “You Only Look Once (YOLO)-mode” permissions—giving an AI broad API access to an entire AWS environment or a SQL database when it only needs one-way read access. In CNI, this privilege creep means an AI error or hijack has a catastrophic blast radius. This is excessive agency.
- Standard security tools look for malicious code. They cannot detect a malicious intent expressed in natural language. If an agent decides to purge a database because it misinterpreted a goal, it looks like a legitimate administrative action to a firewall.
- As these systems become more efficient, humans stop verifying and start rubber-stamping. This automation bias creates a window where an agent can drift from its safety parameters for weeks before a human notices.
To protect CNI, we must move beyond chat-log filters. Safety-critical agentic AI requires Sandboxed Execution—where AI-generated code is run in an isolated environment—and Semantic Guardrails that enforce Hard No zones (e.g., “This agent can never modify the cooling cycle, regardless of the prompt”). We need to enforce human-in-the-loop processes and close the observability gap. Unless we treat AI agents as digital insiders with zero-trust profiles, we are not just deploying smarter tools; we are deploying autonomous liabilities into the heart of our critical infrastructures.
