Episode Details

⬥GUESTS⬥

Sandy Dunn, Consultant Artificial Intelligence & Cybersecurity, Adjunct Professor Institute for Pervasive Security Boise State University | On Linkedin: https://www.linkedin.com/in/sandydunnciso/

Rock Lambros, CEO and founder of RockCyber | On LinkedIn | https://www.linkedin.com/in/rocklambros/

Host: Sean Martin, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining CyberSecurity Podcast [@RedefiningCyber] | On ITSPmagazine: https://www.itspmagazine.com/sean-martin

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⬥EPISODE NOTES⬥

The rise of large language models (LLMs) has reshaped industries, bringing both opportunities and risks. The latest OWASP Top 10 for LLMs aims to help organizations understand and mitigate these risks. In a recent episode of Redefining Cybersecurity, host Sean Martin sat down with Sandy Dunn and Rock Lambros to discuss the latest updates to this essential security framework.

The OWASP Top 10 for LLMs: What It Is and Why It Matters

OWASP has long been a trusted source for security best practices, and its LLM-specific Top 10 is designed to guide organizations in identifying and addressing key vulnerabilities in AI-driven applications. This initiative has rapidly gained traction, becoming a reference point for AI security governance, testing, and implementation. Organizations developing or integrating AI solutions are now evaluating their security posture against this list, ensuring safer deployment of LLM technologies.

Key Updates for 2025

The 2025 iteration of the OWASP Top 10 for LLMs introduces refinements and new focus areas based on industry feedback. Some categories have been consolidated for clarity, while new risks have been added to reflect emerging threats.

• System Prompt Leakage (New) – Attackers may manipulate LLMs to extract system prompts, potentially revealing sensitive operational instructions and security mechanisms.

• Vector and Embedding Risks (New) – Security concerns around vector databases and embeddings, which can lead to unauthorized data exposure or manipulation.

Other notable changes include reordering certain risks based on real-world impact. Prompt Injection remains the top concern, while Sensitive Information Disclosure and Supply Chain Vulnerabilities have been elevated in priority.

The Challenge of AI Security

Unlike traditional software vulnerabilities, LLMs introduce non-deterministic behavior, making security testing more complex. Jailbreaking attacks—where adversaries bypass system safeguards through manipulative prompts—remain a persistent issue. Prompt injection attacks, where unauthorized instructions are inserted to manipulate output, are also difficult to fully eliminate.

As Dunn explains, “There’s no absolute fix. It’s an architecture issue. Until we fundamentally redesign how we build LLMs, there will always be risk.”

Beyond Compliance: A Holistic Approach to AI Security

Both Dunn and Lambros emphasize that organizations need to integrate AI security into their overall IT and cybersecurity strategy, rather than treating it as a separate issue. AI governance, supply chain integrity, and operational resilience must all be considered.

Lambros highlights the importance of risk management over rigid compliance: “Organizations have to balance innovation with securi