The 2026 OWASP Agentic Top 10: Why Agentic AI Security Has to Be Planned Up Front

Agentic AI is moving quickly from experimentation into production. These systems do more than respond to prompts. They plan work, reason through tasks, call tools, and take action across APIs, data stores, and infrastructure. Once a system can act on its own, the consequences of mistakes change immediately.

That is the context in which the Open Worldwide Application Security Project (OWASP) released the 2026 OWASP Top 10 for Agentic Applications. The framework documents the most common and most damaging vulnerability patterns emerging in autonomous AI systems, along with guidance for mitigating them.

For teams building or operating agentic AI, this should not be treated as background reading. It is foundational. At SPR, we see this list less as a prediction of future risk and more as a reflection of what teams are already running into.

Traditional application security assumes a predictable loop. A user makes a request. The system responds. Control returns to the user.

Agentic systems do not work that way.

An agent can set goals, decompose tasks, maintain state, and invoke tools without ongoing human input. A small mistake in identity scoping or tool access does not stay contained. It can cascade into actions that were never intended and are difficult to unwind once they start.

I believe many teams underestimate this risk early on because initial validation focuses on correctness. Does the agent respond appropriately? Does it complete the task? Those are necessary questions, but they are not sufficient ones.

Safety shows up later, often when it is more expensive to address.

The 2026 OWASP Agentic Top 10 identifies ten categories of risk that consistently appear in autonomous AI systems, including:

• Agent goal hijacking
• Tool misuse and unintended execution
• Identity and privilege abuse
• Missing or weak guardrails
• Sensitive data disclosure
• Data poisoning
• Resource exhaustion and runaway execution
• Supply chain vulnerabilities
• Advanced prompt injection
• Over-reliance on autonomous decision making

None of these are edge cases. They reflect patterns that emerge once agents are connected to real systems with real permissions. The value of the OWASP list is that it gives teams a shared language and a concrete way to evaluate risk before deployment, not after an incident.

In my estimation, many custom AI systems and internal autonomous workflows are especially vulnerable. 

These AI systems usually start small. A team builds an agent to solve a narrow problem. It begins as a proof of concept. The output looks good. Stakeholders see value. The system quietly becomes part of a production workflow. 

Inexperienced AI teams often overlook how much effort it takes to get an AI system tuned properly. Why? Because agentic AI is non-deterministic. Teams spend a surprising amount of time tuning prompts, refining workflows, managing edge cases, and iterating until the system behaves consistently enough to trust. That work consumes budget faster than expected. 

I believe this is where a familiar pattern takes hold. 

Security in agentic AI projects often mirrors how software testing has historically been treated. Teams invest heavily in development. Testing happens with whatever time and money remain. When the budget runs out, testing is compressed or deferred. 

Security becomes the same animal. 

By the time the agent finally behaves the way the team wants, there is relief. The system works. The project feels complete. In reality, the security work has barely started. 

We have seen teams reach this point with little time or funding left to properly implement identity boundaries, permission scoping, guardrails, monitoring, auditability, or threat modeling. The agent behaves correctly, but it operates with broad access and limited oversight. 

That is especially risky because correctness is not the same as safety. An agent can do exactly what it was designed to do and still create serious exposure if it has too much autonomy or too few constraints. 

This is why the OWASP Agentic Top 10 should not be treated as aspirational guidance. For any agentic AI system running in production, following these recommendations is table stakes. 

At SPR, we see a clear gap between successful AI experiments and production-ready systems. Proof of concepts demonstrate value quickly. Risk enters when those same systems are promoted without the discipline applied to other enterprise platforms.

Our approach is shaped by decades of building systems that must hold up under real operational pressure.

We treat agentic AI as software that must fail safely. Autonomy is deliberate. Permissions are constrained. Behavior is observable.

In our delivery, this shows up in concrete ways:

• Security-first architecture
  Agents are built with explicit boundaries. Tool access is tightly scoped. Execution environments are isolated. Least privilege is enforced from the beginning.
• Threat modeling grounded in OWASP
  The 2026 OWASP Agentic Top 10 becomes a working checklist, not a poster on the wall. Every agent, integration, and data path is reviewed against known failure patterns.
• Intentional limits on autonomy
  Not every decision should be automated. We help teams define where human approval is required, especially when actions affect data integrity, financial outcomes, or infrastructure.
• Observability and auditability
  Agent decisions, tool calls, and state transitions are logged and explainable. This supports security review, compliance needs, and operational confidence.
• Production hardening
  Many teams build agents like prototypes. SPR helps harden these systems so they can operate reliably and predictably at scale.

Agentic AI can deliver real business value. It also introduces risk that cannot be cleaned up at the end of a project.

I believe organizations that treat security as something to address once the AI works are setting themselves up for painful tradeoffs later. Security that depends on leftover budget or remaining time is security that will always be incomplete.

The OWASP Agentic Top 10 exists to counter that reality. It provides a clear foundation for building agentic systems that are secure by design.

Agentic AI is powerful. For production systems, security is not optional. It is part of the cost of doing it right.