How Agentic AI Is Changing the Cyberattack Surface

An AI chatbot that answers questions is a different risk category entirely from an AI agent that can read your email, query a database, and take action on what it finds, without a human approving each step. That second category, agentic AI, is what security agencies and researchers spent 2026 warning about, and the warning is specific enough to act on now rather than wait out.

The first joint government warning on this specific risk

In May 2026, CISA, the NSA, and their counterparts in Australia, Canada, New Zealand, and the UK published Careful Adoption of Agentic Artificial Intelligence Services, the first joint Five Eyes guidance written specifically for agentic systems rather than AI in general. The advisory groups the real risk into five categories: privilege compromise, design and configuration flaws, behavioral misalignment, structural cascading failures, and accountability gaps, and its core recommendation is to adopt agentic AI incrementally, starting with low-risk tasks and expanding only after an organization can actually demonstrate stable behavior and adequate monitoring, not on a vendor's rollout timeline.

Why every added capability is also a new way in

OWASP's own Top 10 for Agentic Applications, released in December 2025 after input from over 100 security researchers and reviewed by an expert board including representatives from NIST and the European Commission, names agent goal hijacking, tool misuse, and identity and privilege abuse as the leading risk categories. The pattern underneath all three is the same one the CISA guidance flags: every tool, plugin, or data source you connect to an agent to make it more useful also becomes a new path an attacker can try to exploit, and an agent's own permissions inherit into whatever it was given access to complete its task.

The access-control problem nobody's inventory was built for

Every agentic AI deployment creates a non-human identity that needs API access and machine-to-machine authentication, at a scale legacy identity and access systems were never designed to track. That's a genuinely new category sitting alongside human user accounts and service accounts, and it's exactly the kind of exposure our LLM and prompt injection work already tests for in deployed AI systems, just applied to a system that doesn't only generate text, it takes real actions with real permissions.

How seriously to take this right now

Gartner's 2026 cybersecurity predictions name AI agents as a genuinely new attack surface requiring stronger governance, not a theoretical future concern. A widely cited figure, that 48% of security professionals expect agentic AI to be the top attack vector by the end of 2026, comes from a Dark Reading reader poll rather than a controlled survey, so treat the specific number as a sentiment signal rather than a hard statistic. The direction it signals is the part that matters, security teams are genuinely worried about this, and the CISA guidance landing the same year is the strongest evidence that worry is well placed.

Where to start if you're deploying agentic AI now

A policy document alone won't catch this. The practical first step is an honest inventory of which of your own tools already have agentic capability (many SaaS platforms have added it quietly), what permissions each one actually holds, and whether anyone's reviewing the actions they take autonomously. Our AI Services team tests deployed AI systems, including agentic ones, against this exact risk model rather than treating "AI security" as synonymous with testing a chatbot's prompt filters.