Snyk Launches AI Security Engineer Foundations & Certification

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Snyk has introduced a six-module training curriculum titled AI Security Engineer Foundations. Delivered online and in person, the half-hour sessions address security across agentic workflows, MCP architectures, and AI-assisted development.

Key points

  1. The program features six 30-minute sessions offering individual badges and a certificate of completion validating real-world AI security skills.
  2. Module 1 focuses on the OWASP Top 10 for Agentic Applications, covering risks such as Agent Goal Hijacking, Tool Misuse, and Memory Poisoning.
  3. Module 2 explores Shadow AI governance and the generation of AI Bills of Materials (AIBOM) via the Snyk CLI and the Evo platform.
  4. Module 3 covers AI threat modeling tailored to probabilistic, non-deterministic systems across the machine learning lifecycle.
  5. Module 4 examines Model Context Protocol (MCP) security, tool poisoning attacks, ToxicSkills research, and scanning with MCP-Scan and Snyk Code.
  6. Modules 5 and 6 target the security implications of vibe coding (including slopsquatting and the Lobster Ecosystem) as well as AI penetration testing and EVO Continuous Offensive Security (COS).

Why it matters

As autonomous agents and MCP-connected development tools gain broad adoption, traditional AppSec frameworks leave critical gaps. Snyk's curriculum structures remediation strategies for emerging vectors like context poisoning, tool execution, and supply-chain threats in agent skill repositories.

Analysis

Snyk's foundation program directly targets 2025–2026 security developments, especially around agent execution layers and the Model Context Protocol (MCP). The content highlights that agents executing actions require distinct trust boundaries, scoped permissions, and dedicated tooling—such as MCP-Scan alongside standard static analysis—differentiating agentic threats from traditional web vulnerabilities.

Mentioned links

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Summary generated by AI (Gemini 3.8 Flash, 12 October 2026). It may contain errors – the original source is authoritative.

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