The year 2026 marks a pivotal shift in how we interact with artificial intelligence. We have moved rapidly past the era of simple chatbots—systems that merely predicted the next word—into the era of Agentic AI. These are systems that plan, decide, and act. They don’t just talk; they execute.

While this autonomy unlocks incredible value for finance, healthcare, and defense, it introduces a terrifying new attack surface. If you are building, deploying, or securing AI agents, the old playbooks (like the standard LLM Top 10) are no longer enough. In this deep dive, we’ll explore why Agentic AI requires a completely new security paradigm and share the critical risks you need to mitigate today.

Beyond Chatbots: The Rise of Autonomy

To understand the security risks, you first have to understand the architecture. Unlike a standard Large Language Model (LLM) that waits for a prompt and gives an answer, an Agentic Application operates with a degree of independence.

As highlighted in the OWASP GenAI Security Project, agents possess the ability to:

  • Plan: Break down complex goals into steps.
  • Decide: Choose which tools to use without human intervention.
  • Act: Execute code, call APIs, and manipulate data across systems.

This autonomy is the double-edged sword. When an agent acts on behalf of a user, it often does so across multiple steps and systems. If an attacker can influence that agent, they aren’t just getting a rude response—they are potentially wiring money, deleting databases, or poisoning supply chains.

The Core Concept: “Least Agency”

You have likely heard of the security principle “Least Privilege.” The core advice to organizations is simple yet profound: Avoid unnecessary autonomy.

Deploying agentic behavior where it isn’t strictly needed expands your attack surface without adding value. If a task can be done with a deterministic script, don’t use an AI agent. If an agent only needs to read data, do not give it the “agency” to write or delete. Without strong observability into why an agent is choosing a specific tool, that unnecessary autonomy can turn a minor bug into a catastrophic, system-wide failure.

The Agentic Top 10 at a Glance

The new list identifies the ten most critical security risks facing agentic systems. While some overlap with previous LLM risks, they have evolved significantly in this new context.

1. ASI01: Agent Goal Hijack

This is the evolution of prompt injection. In a chatbot, injection might make the bot say something offensive. In an agent, Goal Hijacking manipulates the agent’s actual objectives. Attackers use indirect prompts (embedded in websites or emails) to redirect the agent’s “thought process,” forcing it to exfiltrate data or misuse tools while believing it is following orders.

2. ASI02: Tool Misuse and Exploitation

Agents are only as dangerous as the tools they wield. This vulnerability occurs when an agent uses a legitimate tool (like an email connector or database query) in an unsafe way. It’s not about the tool being broken; it’s about the agent being tricked into using it to delete valuable data or over-invoke costly APIs.

3. ASI03: Identity and Privilege Abuse

Agents suffer from an “attribution gap.” They often operate with high-level permissions but lack a distinct identity of their own. This risk involves attackers exploiting this dynamic trust, using the agent as a “confused deputy” to escalate privileges or access data the user shouldn’t see.

4. ASI04: Agentic Supply Chain Vulnerabilities

Your agent isn’t an island. It relies on third-party tools, models, and even other agents. This risk highlights the dangers of Runtime Supply Chains. Unlike static software libraries, agents might dynamically load new capabilities or connect to other agents (Agent-to-Agent) that have been compromised or booby-trapped.

5. ASI05: Unexpected Code Execution (RCE)

“Vibe coding” is popular, but dangerous. Agents often generate and execute code to solve problems. If an attacker can influence the code generation or the execution environment, they can achieve Remote Code Execution (RCE), escaping sandboxes and compromising the host system.

6. ASI06: Memory & Context Poisoning

Agents need memory to function over time. If an attacker can plant false information in the agent’s long-term memory (via RAG or vector stores), they can permanently bias the agent’s reasoning. This “poison” persists across sessions, affecting future decisions and potentially affecting other users.

7. ASI07: Insecure Inter-Agent Communication

We are moving toward multi-agent systems. When Agent A talks to Agent B, that communication channel must be secure. Weak controls here allow attackers to intercept messages, spoof identities, or inject malicious commands into the workflow of a distributed system.

8. ASI08: Cascading Failures

Because agents rely on each other, a single failure can snowball. A hallucination in a “Planning Agent” can trigger a destructive action in an “Execution Agent.” This risk focuses on the propagation of faults—how a minor error fans out to cause system-wide outages or data leaks.

9. ASI09: Human-Agent Trust Exploitation

Agents are designed to sound human and authoritative. Attackers can exploit this “anthropomorphism.” By manipulating the agent’s output, they can trick the human user into approving dangerous actions, relying on the user’s “automation bias” to bypass security checks.

10. ASI10: Rogue Agents

Perhaps the most futuristic risk: the agent that goes off the rails. Whether due to compromise or misalignment, a rogue agent deviates from its authorized scope. This isn’t just a bug; it’s a persistent, autonomous entity acting deceptively or harmfully within your ecosystem.

Why This Matters for Your Business

The transition to Agentic AI is not just a technical upgrade; it is a governance challenge. Ignoring these risks doesn’t just mean your chatbot might hallucinate—it means your automated systems could be hijacked to perform financial transactions, leak trade secrets, or destroy production data.

Security leaders must start implementing “intent gates,” human-in-the-loop approvals for high-impact actions, and strict sandboxing immediately.

Conclusion

The era of “set it and forget it” AI is over. Agentic systems require continuous monitoring, strict identity management, and a zero-trust architecture. As we explore the rest of this series, we will dive deep into each of these ten risks, providing you with the technical details and mitigation strategies you need to survive the agentic future.