Turning OWASP Top 10 LLM 2025 into a YouTube Series – My Way of Giving Back
Not as a lecture. Not as a jargon-filled presentation. But as a conversation for developers, security engineers, students, builders, and curious minds who want to understand before they implement.
Because let’s be honest: AI is being integrated faster than it is being secured.
And many teams still feel:
- “We know OWASP Top 10 for Web…”
- “We’ve heard of LLM risks…”
- “But how does this actually impact my product?”
This series is my small attempt to bridge that gap.
What this YouTube Series Covers
Each episode dives into one risk from the OWASP Top 10 LLM 2025, broken down with:
- Human-friendly explanations
- Real-world analogies
- Developer + security perspectives
- What you can actually do about it
From Prompt Injection and Training Data Poisoning, to System Prompt Leakage, Vector & Embedding Weaknesses, and Over-reliance on Model Outputs, this series connects standards to real life.
Why I Did This
I’ve spent years teaching, mentoring, and working with amazing communities across security and AI.
What I’ve seen repeatedly is: People don’t lack interest. They lack accessible security knowledge in the AI age.
This series is for:
- That developer trying to secure their first AI feature
- That student curious about AI security careers
- That AppSec engineer navigating unknown AI risks
- That leader trying to understand what to prioritize
If it helps even a handful of people build more secure AI systems, it’s already worth it.
LLM01: Prompt Injection
Manipulating LLMs via crafted inputs can lead to unauthorized access, data breaches, and compromised decision-making.
LLM02: Insecure Output Handling
Neglecting to validate LLM outputs may lead to downstream security exploits, including code execution that compromises systems and exposes data.
LLM03: Training Data Poisoning
Tampered training data can impair LLM models leading to responses that may compromise security, accuracy, or ethical behavior.
LLM04: Model Denial of Service
Overloading LLMs with resource-heavy operations can cause service disruptions and increased costs.
LLM05: Supply Chain Vulnerabilities
Depending upon compromised components, services or datasets undermine system integrity, causing data breaches and system failures.
LLM06: Sensitive Information Disclosure
Failure to protect against disclosure of sensitive information in LLM outputs can result in legal consequences or a loss of competitive advantage.
LLM07: Insecure Plugin Design
LLM plugins processing untrusted inputs and having insufficient access control risk severe exploits like remote code execution.
LLM08: Excessive Agency
Granting LLMs unchecked autonomy to take action can lead to unintended consequences, jeopardizing reliability, privacy, and trust.
LLM09: Overreliance
Failing to critically assess LLM outputs can lead to compromised decision making, security vulnerabilities, and legal liabilities.
LLM10: Model Theft
Unauthorized access to proprietary large language models risks theft, competitive advantage, and dissemination of sensitive information.
Watch the Series
Let me know if any topic needs deeper breakdowns, I’d love to keep this conversation going.
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