Applying the OWASP Top 10 for Agentic Applications to Your AI Agents - Will Velida - NDC Oslo 2026

Applying the OWASP Top 10 for Agentic Applications to Your AI Agents - Will Velida - NDC Oslo 2026

Title history
  • Applying the OWASP Top 10 for Agentic Applications to Your AI Agents - Will Velida - NDC Oslo 2026 — observed on 20261028-0955 UTC (YouTube)
🎙 Will Velida 👥 230K 📅 October 28, 2026 ⏱ 70 min 👁 16 📄 expert opinion 🧭 2026-10-28
Available in: English (current) Français

Keywords

AI agentsOWASPsecurityprompt injectionagent governance

Summary

This talk from NDC Oslo 2026 by Will Velida, a senior software engineer at Microsoft, focuses on applying the OWASP Top 10 for Agentic Applications to AI agents. The speaker begins by referencing recent security incidents such as GitSpy and Dead Bugs to illustrate common architectural weaknesses. He then introduces the OWASP Top 10 framework for agentic systems, covering risks like goal hijacking, tool misuse, identity abuse, memory poisoning, and cascading failures. The core of the talk involves practical demonstrations using Microsoft Foundry and the Agent Governance Toolkit. He shows how to protect against goal hijacking using prompt shields and how to enforce tool misuse policies. The talk emphasizes treating all LLM output and external content as untrusted, implementing least-privilege access, and using middleware for intent validation. The speaker provides concrete code and architecture decisions, such as input validation on tool parameters, identity separation with Entra Agent ID, and circuit breakers to prevent costly cascading failures. The overall message is that security must be built into the agent lifecycle from design to deployment.

173 words

Critical Evaluation

Value of the Information & Strength of the Argument

The talk provides high-value, practical information for securing AI agents. The argumentation is strong, supported by real-world examples and live demonstrations. The speaker effectively bridges the gap between theory (OWASP Top 10) and implementation, offering actionable controls and best practices. The emphasis on treating LLM output as untrusted and implementing layered security is well-argued and grounded in concrete scenarios. The demonstrations illustrate the vulnerabilities and the mitigations clearly, making the content both engaging and instructive.

Scientific Rigor, Source Quality, Title Accuracy

The sources referenced include OWASP (the framework) and Microsoft’s own tools and documentation, which are credible for the topic. The talk is based on the speaker’s expertise and practical experience. The title is perfectly aligned with the content, as the talk systematically applies each applicable OWASP risk to the speaker’s health agent example. No audience comments were provided, so no analysis was performed.

153 words

Title / Content Match

The title accurately reflects the content, which applies the OWASP Top 10 for Agentic Applications to AI agents with concrete examples and mitigations.

Quality & Reliability

8/10

Presentation by a Microsoft senior software engineer with practical demos and references to OWASP, but not peer-reviewed.

Key Moments

Cited Sources

  • NDC Conferences — The conference website where the talk was recorded.
  • NDC Oslo — The specific NDC Oslo event page.

Concurring Sources

Contribution & Novelties

The talk contributes practical, actionable advice on applying OWASP Top 10 for Agentic Applications to real AI systems, using concrete examples and demonstrations. It stresses the importance of treating all natural language inputs as untrusted and implementing layered security controls such as goal immutability, least-privilege identities, and policy enforcement. The speaker’s use of a personal health agent case study makes the concepts relatable and directly applicable.

Pour aller plus loin :

  • OWASP Top 10 for LLM Applications — The broader OWASP framework for LLM security, foundational to the agentic version.
  • Microsoft Agent Governance Toolkit (no URL provided due to uncertainty) — An open-source toolkit for enforcing policies and auditing agent actions.
  • Prompt injection attacks on Wikipedia — A general reference on the attack technique that underlies many of the risks discussed.

131 words

Radar Profile

The radar profile shows consistently high scores across all dimensions, indicating a technically deep, well-sourced, and reliable presentation with ample information. The talk balances quality and quantity effectively, making it a strong resource for practitioners.

Reliability 8/10