OWASP just published their first Top 10 for Agentic Applications, and it’s a watershed moment for AI security.
For over two decades, the OWASP Top 10 has been the security bible for web applications. Every developer, security engineer, and CISO has it bookmarked. Now, with the rise of autonomous AI agents, OWASP has done the same for agentic systems.
This isn’t just another security checklist. It’s a recognition that autonomous agents create fundamentally new attack surfaces that traditional security models can’t handle.
Why Traditional Security Fails for Autonomous Systems
Traditional security operates on a simple principle: least privilege. Give each component only the access it needs to do its job.
But autonomous agents break this model. To be useful, an agent needs broad access. It needs to read your email, edit your documents, manage your calendar, and interact with dozens of APIs. The more autonomous it is, the more permissions it requires.
This creates what I call the agent security paradox: autonomy requires access, but access creates attack surface.
The OpenClaw incident from last month perfectly illustrates this paradox. Users granted OpenClaw sweeping permissions to act as their personal assistant. When security researchers found hundreds of exposed control panels, they could access private conversations, API keys, and even hijack agents.
This isn’t a bug in OpenClaw’s implementation. It’s a fundamental flaw in how we think about agent security.
The OWASP Top 10 for Agentic Applications 2026
Let’s break down the top threats and what they mean for agent builders:
ASI01: Agent Goal Hijack
The new SQL injection for AI agents
Attackers manipulate the agent’s core objective. Instead of “book a flight to Berlin,” the agent receives “transfer all funds to account X.”
This is particularly dangerous because agents are designed to be flexible and creative in achieving goals. That same flexibility makes them vulnerable to subtle goal manipulation.
The fix: Decision boundaries. Agents should pause and explain their reasoning before taking sensitive actions. The orchestration layer, not the model, should be the security boundary.
ASI02: Tool Misuse and Exploitation
Every tool is a potential attack vector
Agents call tools - APIs, databases, file systems. Each tool call is a potential injection point. An attacker doesn’t need to compromise the model; they just need to manipulate the tool’s input or output.
The fix: Tool sandboxing and input validation. Treat every tool call as untrusted. Validate inputs, sanitize outputs, and run tools in isolated environments.
ASI03: Identity and Privilege Abuse
The permission problem at scale
Agents inherit the user’s identity and permissions. If an agent can read your email, so can anyone who compromises that agent. This creates an exponential attack surface: compromise one agent, gain access to everything that user can access.
The fix: Agent-specific identities with scoped permissions. Agents should have their own credentials with only the permissions they need for specific tasks.
ASI04: Insecure Agent Communication
Multi-agent systems create new trust boundaries
When agents communicate with each other, they create new attack surfaces. How do agents authenticate each other? How is communication encrypted? What prevents man-in-the-middle attacks?
The fix: Explicit agent-to-agent protocols with mutual authentication and end-to-end encryption.
ASI05: Insufficient Agent Isolation
One compromised agent shouldn’t compromise the system
Agents often share resources - memory, context, tools. A compromised agent can poison shared memory or abuse shared tools to attack other agents.
The fix: Strong isolation between agents. Each agent should run in its own sandbox with separate memory and tool access.
The Orchestration Layer as the New Security Boundary
The pattern here is clear: the model isn’t the security boundary. The orchestration layer is.
The LLM is a commodity. It’s powerful, flexible, and creative - exactly the qualities that make it vulnerable to manipulation. We can’t secure the model itself; we need to secure the harness around it.
This is why I’ve been beating the drum about the orchestration layer for the past year. The breakthrough insight isn’t making agents more intelligent; it’s making them more appropriately constrained.
Practical Security Patterns for Agent Builders
Based on my experience building agent systems for enterprise compliance, here are the patterns that actually work:
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Decision Boundaries Explicit checkpoints where agents must pause and explain reasoning before proceeding. The orchestration layer evaluates whether the proposed action aligns with security policies.
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Tool Sandboxing Every tool runs in an isolated environment with minimal permissions. Tools can’t access each other’s data or the agent’s internal state.
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Structured Output Validation Agents must output actions in a structured format (JSON schema) that’s validated before execution. This prevents prompt injection from bypassing security checks.
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Audit Trails Every action, every tool call, every decision is logged with full context. When something goes wrong, you can reconstruct exactly what happened.
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Human-in-the-Loop Triggers Certain actions always require human approval. The orchestration layer knows which actions are high-risk and escalates them automatically.
The Future of Agent Security
The OWASP Top 10 for Agentic Applications marks a turning point. We’re moving from the “wild west” phase of AI agents to the “responsible deployment” phase.
Security is no longer an afterthought. It’s the foundation that determines which agents succeed in production and which get canceled before they ever ship.
The most successful agents in 2026 won’t be the most autonomous. They’ll be the most appropriately constrained. They’ll have the right balance of capability and control, flexibility and security.
If you’re building agents, print this list and put it on your wall. These aren’t theoretical threats; they’re the attacks that are happening right now. And they’re only going to get more sophisticated as agents become more powerful.
The good news? We have the patterns to build secure agents. We just need to use them.
Hugo Nogueira is CPTO at Complyance, where he builds AI agent systems for enterprise compliance. He writes about AI agent architecture and security at hugo.im.