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Security gets sharper when we stop treating AI like magic and start treating it like an untrusted user. We sit down with Eric Galinkin to unpack the real-world ways red teams and defenders are using language models today, where they fall apart, and how to build guardrails that hold up under pressure. From MCP servers that look a lot like ordinary APIs to the messy truths of model hallucination, this conversation trades buzzwords for practical patterns you can apply right now.

Eric shares takeaways from Offensive AI Con: how models help triage code and surface likely bug classes, why decomposed workflows beat “find all vulns” prompts, and what happens when toy benchmarks meet stubborn, real binaries. We explore reinforcement learning environments as a scalable way to train security behaviors without leaking sensitive data, and we grapple with the uncomfortable reality that jailbreaks aren’t going away—so output validation, sandboxing, and principled boundaries must do the heavy lifting.

We also dig into Garak, the open-source system security scanner that targets LLM-integrated apps where it hurts: prompted cross-site scripting, template injection in Jinja, and OS command execution. By mapping findings to CWE, Garak turns vague model “misbehavior” into concrete fixes tied to known controls. Along the way, we compare GPT, Claude, and Grok, talk through verification habits to counter confident nonsense, and zoom out on careers: cultivate niche depth, stay broadly literate, and keep your skepticism calibrated. If you’ve ever wondered how to harness AI without handing it the keys to prod, this one’s for you.

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