Steve Henderson, Ph.D.

A lot of the work I do lives in a place cloud LLMs can’t go. Incident writeups, reverse engineering, artifact analysis, internal vuln-triage notes — the threat-intel workflows that would benefit most from an AI assistant are exactly the ones where pasting text into somebody else’s API is a no-go. Most LLM benchmarking quietly ignores this, because the benchmarks assume you can just call GPT. In my world you often can’t.

I build and think a lot about cyber agents — AI systems that read code, call tools, touch infrastructure, and increasingly do real work without a human watching every step. So when Google DeepMind published GDM AI Control Roadmap (v0.1) (Phuong, Jenner, Simon, Ho, Shah, Farquhar & Coull, 2026), it piqued my interest. It’s the clearest articulation I’ve seen of a simple, slightly uncomfortable idea: the most useful framing for securing AI agents is to treat them as a potential insider threat — and to borrow, almost wholesale, the playbook we already use against malicious employees.

On Friday, March 29, 2024, a Microsoft engineer named Andres Freund sent an email to the oss-security mailing list that quietly averted what might have been the most consequential supply-chain compromise in the history of open source. He had been chasing a performance oddity — SSH logins on a Debian test system were running about half a second slower than they should have, and liblzma was burning suspicious amounts of CPU. What he found at the bottom of that rabbit hole was a deliberately planted backdoor in XZ Utils, a compression library that ships in virtually every Linux distribution on Earth.

A condensed walkthrough of the 42Challenge boot-to-root box from VulnHub. The fun of this one isn’t a single CVE — it’s chaining a chain of small weaknesses: a client-side filter, a local file include, log poisoning, a backup file, and a little reverse engineering. The methodology generalizes well beyond this box.

Introduction