AI Agents Are Security Holes Waiting to Happen
GitLost published a writeup showing they tricked GitHub Copilot's AI agent into leaking private repository data. The mechanism is not complicated: LLMs with access to private information and a public-facing interface are, by design, a social engineering target. The comment threads are blunt about it. One commenter put it plainly: 'Who thought having an LLM with access to private information, with public access to ask it questions, would ever be a secure process?'
This is not a GitHub-specific bug. It is a category-level problem. Every AI agent that gets wired to sensitive data and then exposed to untrusted inputs faces the same attack surface. The fix GitHub eventually applied does not change the underlying architecture, it just patches one exploit path.
The counterpoint in the thread is that responsible disclosure happened, the issue is fixed, and the risk window was finite. That is technically true. But the deeper worry is that this class of vulnerability scales with how many AI agents get deployed with access to sensitive systems, and that number is going up fast.
So what?
If you are building AI agents with access to user data or private company systems, you need to treat the agent's public interface as an attack surface from day one, not after your first breach. Assume adversarial prompting is the default, not the exception. The GitHub incident is a preview of what happens when you don't.