For years, most conversations about AI risk focused on models: how they’re trained, how they hallucinate, and how attackers might manipulate prompts or data. That focus made sense when AI systems primarily generated outputs. But as these systems begin to plan, make decisions, and act across workflows, the limits of a model-only view are becoming clear.

The release of the OWASP Top 10 for Agentic Applications for 2026 reflects this point. It’s more than a new vulnerability list; it signals a broader shift in how AI security must be understood, moving beyond model integrity to the governance of AI-driven behavior.

The future of AI security won’t be defined by accuracy alone. It will be shaped by how systems behave, how trust is applied, and what outcomes those decisions produce.

Agentic AI systems are fundamentally different from traditional AI applications. Instead of responding to a single prompt, agents can pursue goals, invoke tools, retain context, and operate across systems with limited or no human intervention. That autonomy enables powerful new use cases, but it also changes the nature of failure.

In a traditional AI system, a failure usually means a bad output. In an agentic system, a failure can mean an action—sending a message, changing a configuration, transferring data, or triggering a workflow—with real operational impact.

In the recent EchoLeak incident, an attacker sent a single crafted email that silently triggered Microsoft 365 Copilot to process attacker-controlled instructions. Without any user interaction, the AI agent could be coerced into disclosing confidential emails, files, and chat logs, operating entirely within the permissions it already had. Nothing was “clicked.” No malware was installed. The system behaved exactly as designed, just not as intended.

One of the clearest signals in the Agentic Top 10 is where risk actually shows up.

Many of the highest-impact threats OWASP highlights, like goal hijacking, tool misuse, identity abuse, and cascading failures, aren’t about the model getting something “wrong.” They’re about how systems behave over time, especially when context, memory, or trust is compromised.

Prompt injection offers a clear example of this shift. Attackers manipulate inputs to override intended behavior—bypassing safeguards, leaking sensitive data, or triggering unauthorized actions. More advanced reprogramming embeds persistent instructions that alter behavior over time, redirecting outputs or enabling automated social engineering.

In agentic systems, this kind of manipulation becomes even more consequential. When altered behavior persists and compounds across tasks, tools, and sessions, risk is no longer confined to a single interaction. It unfolds over time, driven not by isolated errors, but by systems acting on compromised intent.

As agents become more capable, autonomy itself becomes a risk multiplier. Agentic systems increasingly operate across multiple tools and environments, persist memory across sessions, delegate tasks to other agents, and act at machine speed, which is far faster than human review cycles can realistically keep up. That combination increases blast radius. A single compromised input, mis-scoped permission, or trusted integration can cascade across systems before anyone notices.

OWASP’s introduction of concepts like least agency reflects this reality. Just as least privilege limits what a system can access, least agency limits what it should be allowed to do.

Not every system needs full autonomy, and granting it without clear boundaries increases risk without adding value.

While autonomy may create a new attack surface, trust creates the real vulnerability.

Agentic systems operate in environments rich with implicit trust: trusted tools, trusted identities, trusted integrations. When those systems act on behalf of humans, they inherit not just access, but authority.

We’ve already seen attackers exploit this dynamic by abusing trusted AI tools and integrations, not by hacking them directly, but by manipulating how they’re used. When systems assume that trusted tools will always be used appropriately, attackers gain leverage by steering behavior rather than breaching defenses.

This is where agentic risk becomes uniquely dangerous. Agents don’t just execute instructions—they interpret intent. They recommend actions. They explain decisions. And when that reasoning appears confident or authoritative, humans are more likely to approve outcomes they wouldn’t otherwise accept.

Traditional security models tend to emphasize control: block the threat, prevent the action, stop the breach. But autonomous systems don’t operate well under constant manual control, and attempting to enforce it often introduces blind spots rather than reducing risk.

The OWASP Agentic Top 10 points toward a different approach where visibility and governance matter more than absolute prevention. Securing agentic systems means understanding agent behavior—what agents are doing and why, defining clear boundaries around their goals and actions, maintaining auditability and explainability, and designing systems to contain impact when failures occur.

This is about evolving security to meet autonomy and focusing on outcomes that keep systems safe, effective, and trustworthy.

The OWASP Agentic Top 10 isn’t a warning to slow down AI adoption. It’s a blueprint for deploying autonomous systems responsibly. Autonomy is powerful, but it must be intentional; behavior matters more than correctness; and least agency is just as critical as least privilege. As agents act at machine speed across tools and workflows, visibility becomes the foundation for effective governance, while human oversight remains essential—especially for decisions with irreversible or high-impact consequences.

The future of AI security is already being shaped by how systems act, how trust is assigned, and how failures propagate. As AI systems move from responding to acting, security must evolve alongside them. Organizations that adapt now won’t just reduce risk, they’ll help define what safe, trustworthy AI looks like in an autonomous world.

Interested in learning more about the future of AI security and how Abnormal uses behavioral AI to protect your organization? Schedule a demo today!