AI Security Engineer Foundations + Certificate — Javier Garza, Snyk

Published 2026-10-11 · Duration 1:57:34

Summary

This workshop provides a comprehensive overview of AI security engineering, detailing the shift from traditional software security models to address the unique risks posed by Large Language Models (LLMs) and agentic workflows. Key areas covered include the OWASP Top 10 for LLMs, securing the supply chain of AI components (AI Bomb), and implementing proactive AI-native threat modeling. The session emphasizes that security must be integrated early in the design lifecycle, requiring continuous monitoring, strict guardrails, and human-in-the-loop processes to manage non-deterministic AI behavior.

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Key takeaways

AI Systems are Non-Deterministic 21:40
Unlike traditional software, AI systems are probabilistic. This necessitates a fundamental shift in threat modeling, moving from reactive scanning to proactive, AI-centric design and continuous iteration.
AI Bomb (CBEM) is Essential for Inventory 26:40
An AI Bill of Materials (AI Bomb) is required to create a searchable inventory of all AI assets—including models, datasets, agents, and pipelines—to track vulnerabilities and impact in the event of a supply chain attack.
Defense Requires Guardrails and Education 16:40
Technical controls (like sanitizing inputs and using function calls) must be paired with organizational guardrails, developer training, and clear policies to prevent workarounds and misuse.
Secure Development Requires Hooks and CI/CD Integration 30:00
Security checks must be integrated into the development pipeline (CI/CD) using hooks and agent rules to automatically scan generated code and prevent vulnerabilities before deployment.

Technical details

OWASP Top 10 for LLMs 901s
Key risks include Prompt Injection (manipulating LLM behavior, e.g., using 'Gandalf' techniques), Sensitive Information Disclosure (LLMs revealing proprietary data), and Supply Chain Vulnerabilities (attacking open-source libraries or third-party models).
Prompt Injection Techniques 901s
Attacks can be direct (manipulating the system prompt) or indirect (embedding malicious text, e.g., white text on a white background, into external content like a LinkedIn profile). Defenses include sanitizing input and requiring human approval for high-risk actions.
Agent Architecture Components 1100s
An Agent is the interactive tool; the LLM is the 'brain' (the large language model); and an MCP (Model Communication Protocol) tool enhances the agent's abilities by connecting it to external APIs for real-time data (e.g., weather).
MCP Security and Tool Poisoning 2100s
MCP servers are susceptible to attacks like tool poisoning, where hidden instructions are embedded to execute malicious actions (e.g., sending API tokens). Developers must treat MCP servers like any other code and use specialized tools to scan for malware.
AI Threat Modeling 1300s
This is a proactive process that identifies, analyzes, and mitigates threats early in the design lifecycle. It requires defining trust boundaries for AI inputs (e.g., external data from an API) and ensuring data flow diagrams are updated as the architecture evolves.

Mentioned resources

aiccurity.engineer (Workshop/Certification Platform)
snyk.io (Security Tool/Platform)
gandalf.lacera.ai (Demonstration Tool)

Channel & topics

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