Topic starter
02/08/2026 12:10 pm
TL;DR: OWASP released the first Top 10 for Agentic Applications 2026, tying real incidents to failure patterns such as indirect prompt injection, overprivileged tools, poisoned supply chains, and memory poisoning in autonomous AI systems, according to Anomali. The governance challenge is no longer model safety alone but the delegated authority, trust chains, and persistent state that conventional controls were never built to contain.
Editorial analysis by NHI Mgmt Group, based on content published by Anomali: “Anomali Cyber Watch: OWASP Agentic AI, MongoBleed, WebRAT Malware, and more”.
Key questions
Q: How should teams govern agentic AI when the model can act across multiple tools and services?
A: Teams should govern the full execution path, not just the model endpoint.
Q: Why do overprivileged tools make agentic AI harder to secure?
A: Because a tool with more access than the task requires turns a prompt into an execution path.
Q: What are the signs that agent memory or context controls are failing?
A: Look for inconsistent behaviour across tasks, decisions shaped by stale or unexpected context, and outputs that reflect prior malicious prompts long after the original source is gone.
Practitioner guidance
- Inventory every agentic workflow and its delegated authority Map where agents exist, which tools they can invoke, what identities they inherit, and which downstream systems they can touch.
- Restrict tool permissions to task-scoped minimum access Reduce each tool to the narrowest usable action set and separate read, write, and execution capabilities wherever possible.
- Validate prompts, context sources, and retrieval inputs Treat retrieved content, user uploads, and external context as potentially hostile because they can steer agent behaviour indirectly.
Bottom line: Agentic AI changes the control problem by combining autonomy, tools, memory, and delegated trust into one runtime risk surface.
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02/08/2026 1:14 pm
Agentic governance is now an identity problem, not just an AI safety problem. The article shows that the core failures sit in delegated authority, trust chains, and persistent state. Those are the same kinds of governance boundaries IAM and PAM exist to control in human and machine identity programmes. The practical conclusion is that agentic AI must be governed as an identity-bearing system, not as a conversational interface.
A few things that frame the scale:
- Gartner predicts that by the end of 2026, 40% of enterprise apps will feature task-specific AI agents.
A question worth separating out:
Q: What should organisations do when an AI agent depends on external tools and plugins?
A: Apply supplier and dependency governance before deployment and keep monitoring after it goes live. The question is not whether the component is convenient, but whether it can change agent behaviour, expand authority, or introduce hidden trust relationships. If the dependency can alter execution, it belongs in the security review scope.
👉 Read our full editorial: OWASP Agentic AI Top 10 exposes gaps in agent governance