Agentic AI security testing

The shift from generative AI chatbots to autonomous Agentic AI is moving fast. We are no longer just securing models that talk; we are securing systems that act, plan, and execute across our environments. Standard penetration testing and traditional LLM security (like basic prompt injection checks) are no longer enough. If your AI can access APIs, trigger payments, or write code, your security testing needs a massive upgrade. Here is a draft for a post you can use to highlight the new realities of Agentic AI security testing: Title: Why Your LLM Security Strategy Will Fail Against Agentic AI We’ve spent the last few years learning how to secure chatbots. We focused on prompt injection, jailbreaking, and sensitive data leakage. But the game has changed. Enter Agentic AI: autonomous systems that don't just generate text, but actually do things. They string together tasks, access internal APIs, query databases, and make decisions with minimal human oversight. If your security testing is still treating agents like chatbots, you are leaving your organization exposed. The recently released OWASP Top 10 for Agentic Applications (2026) makes it clear that we need a new approach. Here are the four pillars your Agentic AI security testing must cover: 1. Goal Hijacking & Intent Breaking Agents determine their own execution plans. An attacker doesn't need to break the system; they just need to subtly alter the agent's goal via a poisoned prompt or manipulated input. Testing must validate whether an agent can be tricked into deviating from its core objective to perform malicious actions. 2. Tool & API Misuse Simulation Agents are powerful because of the tools they use (CRM access, cloud infrastructure modification, email). Security assessments must simulate unauthorized API calls and test for autonomous privilege escalation. Can a low-privilege agent trick a high-privilege agent (like a finance bot) into executing a transfer? 3. Memory & Context Poisoning Unlike stateless LLMs, agents remember. They cache data and carry context across sessions. Penetration testing must ensure attackers cannot plant malicious payloads or backdoors in an agent’s memory that trigger later when a different user interacts with the system. 4. Cascading Failure & Blast Radius Validation A single fault in one agent can propagate across a multi-agent network. If a Market Analysis agent is poisoned with bad data, will the downstream Execution agent automatically make a massive, unauthorized trade? Testing must map out these workflows and implement strict identity boundaries between agents. The Takeaway: An AI security assessment that only checks for prompt injection is just an automated scan—not a true Agentic AI pentest. As we hand over the keys to autonomous systems, security teams must validate behavior, actions, and workflows, not just inputs and outputs.