Uploaded April 2026 | Updated September 2026, 2 weeks ago
This interactive session transforms AI security education into an engaging courtroom-style debate. We'll present five critical vulnerabilities affecting modern AI development tools, and after each case study, the audience becomes the jury—voting on who's responsible: the careless user, the negligent developer, or the inadequate service provider.
We'll dissect real CVEs including GitHub Copilot's wormable RCE (CVE-2025-53773), MCP server command injections (CVE-2025-53107, CVE-2025-5277), GitHub's private repository leak via prompt injection, and Microsoft Copilot's zero-click data exfiltration (CVE-2025-32711). Each case reveals technical root causes through collaborative analysis.
The conclusion challenges the "blame game" itself: these vulnerabilities expose fundamental architectural weaknesses in agentic AI systems where traditional security models fail. We'll establish that securing AI tools demands a shared responsibility framework—developers must code defensively, providers must architect securely, and users must understand AI-specific risks. The session culminates with actionable best practices for each stakeholder.
This interactive session transforms AI security education into an engaging courtroom-style debate. We'll present five critical vulnerabilities affecting modern AI development tools, and after each case study, the audience becomes the jury—voting on who's responsible: the careless user, the negligent developer, or the inadequate service provider.
We'll dissect real CVEs including GitHub Copilot's wormable RCE (CVE-2025-53773), MCP server command injections (CVE-2025-53107, CVE-2025-5277), GitHub's private repository leak via prompt injection, and Microsoft Copilot's zero-click data exfiltration (CVE-2025-32711). Each case reveals technical root causes through collaborative analysis.
The conclusion challenges the "blame game" itself: these vulnerabilities expose fundamental architectural weaknesses in agentic AI systems where traditional security models fail. We'll establish that securing AI tools demands a shared responsibility framework—developers must code defensively, providers must architect securely, and users must understand AI-specific risks. The session culminates with actionable best practices for each stakeholder.
