Securing Code You Didn't Write: The AI Era's New Threat Model

Anyone can ship a working app in an afternoon now. The AI writes the code, picks the dependencies, configures the infrastructure, and wires up the tools — and software security hasn't caught up.

The coding agent, and everything it trusts — its instructions, its tools, its context — has become part of the software supply chain. Attackers are already exploiting the shift: poisoned configuration files that steer the agent, prompt injection that erases the line between data and commands, and insecure MCP deployments that turn tool access into command execution.

This talk explains what changed in the threat model, why auditing dependencies is no longer enough, and what practical security looks like when no team can hand-review every line of AI-generated code. We'll decode three recent attacks, trace the recurring weakness behind them — prompt injection that no current model reliably resists — and show how security can move into the coding agent's own feedback loop.

You'll leave with a concrete model of AI-era software risk, and a practical way to catch vulnerabilities before they reach production or compromise agents.