This time I sat down with Avi Lumelsky, AI Security Researcher at Oligo Security, where he works at the intersection of AI and runtime protection. Avi’s story is a perfect example of how curiosity leads to innovation. Here are some of the topics we covered:
From inference to insight
Before Oligo, Avi worked at Deci AI, optimizing model inference speed. There, he realized something crucial — performance isn’t just about models; it’s also about how well you understand and leverage the system it runs on.The confinement challenge
Imagine a Python model that should only do math, but could also spawn a subprocess or access the network. How do you confine it safely?Discovering eBPF
His early experiments with DTrace were too slow and invasive for production, so when eBPF matured, he rebuilt his secimport prototype — and found a scalable way to trace and enforce what code can (and can’t) do in real time.Beyond observability
Avi’s big insight: eBPF isn’t just for monitoring. Combined with Linux Security Modules (LSM) and KRSI, it can actively stop malicious behavior before it completes — for example, blocking a roguepickle.load()before it spawns a shell.Language-aware security
At Oligo, Avi’s team extended this concept across languages — Python, Java, Node, .NET, PHP — extracting application-level context straight from production without user-space overhead.From CVEs to context
Instead of flagging every potential vulnerability, Oligo maps which functions actually run in production, reducing noise and focusing developer effort where it matters most.The AI connection
We also discussed how AI agents could soon operate eBPF — dynamically tuning kernel parameters or deploying probes on demand, creating adaptive, self-healing systems.Looking ahead
Avi sees a future where security tooling merges with intelligence — where production data directly informs code fixes, and AI uses eBPF to keep systems resilient in real time.
🐝 I’ll leave it there — hope you enjoy the conversation.
