In this episode, I sit down with Ondrej Vlcek, Founder and CEO at AISLE and the former CEO of Avast, to discuss where AI vulnerability discovery actually stands, including what got commoditized, what did not, and why shipping a fix a maintainer will accept is a very different problem from finding the bug in the first place.
Ondrej spent roughly 30 years in this industry, starting as employee number six or seven at Avast doing kernel-mode driver work on Windows 95, and eventually taking the company public and selling it to NortonLifeLock in a nearly $9 billion transaction. He co-founded AISLE in the fall of 2024, and his team has since disclosed more than 350 CVEs across projects like OpenSSL and curl.
I have been beating the remediation drum for a while now, so this was a conversation I wanted to have with someone who is actually shipping accepted patches upstream rather than just posting finding counts.
We chatted about:
Going from Avast intern to CEO, and why vulnerability management became the next problem worth solving
The jagged frontier, and why a bigger or more expensive model does not reliably mean better results
Which bug classes genuinely got cheap to find, and which subtle ones still lead to XZ Utils and Log4j style outcomes
Why the gray market price of vulnerabilities has gone up rather than collapsed
Building a model-agnostic system, and the bespoke benchmarks AISLE uses to move workloads between models
Sovereign AI, on-prem and air-gapped deployment, and why your findings are more sensitive than your source code
Triage, reachability, and why most findings are not actually exploitable in your environment
Patch verification in bespoke Docker environments, and mitigations for systems that cannot be redeployed
How AISLE earned trust from curl after Daniel Stenberg shut down a seven year bug bounty
Whether a CVE count is a vanity metric, and what makes it a real signal
Build versus buy as the underlying model capability keeps getting cheaper
What breaks first across maintainers, CNAs, and the CVE ecosystem
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Takeaways
Bigger models are not reliably better models
The phrase “jagged frontier” has escaped AISLE’s blog and entered the general discourse at this point, and Ondrej’s explanation of it is the most practical version I have heard. As he put it, “more expensive or bigger or more robust model doesn’t necessarily mean better results.” Large models struggle with certain vulnerability classes while smaller ones, given a harness that supplies prior knowledge of what good looks like, do better on discovery and triage.
The second half of that argument is the one buyers underweight. Cheaper and faster models mean you can run far more of them. “Because of those gains that you get in terms of speed and in terms of cost, you can run many, many more agents at the same time,” Ondrej said, describing swarms of specialized agents that chunk code and coordinate with each other. That is what people mean when they say the system beats the model, and it is why AISLE built its own benchmark to move workloads between models as new versions ship. Anyone building this internally should be asking whether they have that evaluation layer, because without it you are married to whichever model you picked last quarter.
Discovery is cheaper, but it is not solved
I pushed on whether finding bugs is now commoditized, and Ondrej’s answer used the market rather than the models. Vulnerability prices in the gray market have gone up, not down, despite all the model progress. The floor moved, so memory safety bugs and off-by-one errors are much easier and cheaper to find today. The subtle bugs that produce XZ Utils and Log4j outcomes are still hard.
This lines up with the token maxing backlash we saw earlier this year. The industry briefly confused spending more compute with getting better results, and the pricing data suggests the market never believed it.
Remediation is more than generating a patch
This was the heart of the episode for me. Ondrej broke remediation into pieces most vendors collapse into one. Triage comes first, and it matters because “the vast majority of findings may actually not be related or are not directly exploitable in your environment,” which requires data flow context rather than just function reachability. Then comes patch generation, and then the part nobody demos, which is automated verification. AISLE spins up bespoke Docker images to compile the code, run unit tests, and confirm the fix neither reintroduces the vulnerability nor breaks something else.
Then there is the category where a patch cannot be deployed at all. Cars, industrial robots, ATMs. Ondrej’s answer there was compensating controls and network level mitigations rather than pretending a code patch closes the loop. This maps almost exactly to the empirical work Keith Hoodlitt and the team at 1Password published on AI-generated patches, where fixes frequently failed, broke functionality, or introduced new vulnerabilities. Anyone selling you autonomous remediation without a verification story is selling you the easy half.
Quality is what earned curl’s trust
Daniel Stenberg shut down a seven year bug bounty over what he called death by a thousand slops, and a few months later curl was running AISLE internally. Ondrej’s explanation was not clever technology. It was standards. “We would be professionally ashamed to actually send reports that we don’t stand behind,” he said, describing reports that arrive pre-triaged with a working proof of vulnerability, repro steps, and a pre-tested proposed fix. Roughly 15 to 20 reports went into curl, all of them high quality.
That is the whole lesson for anyone pointing AI at open source right now. Maintainers are unpaid volunteers, and the currency you are spending is their attention. Ondrej called it white glove service for maintainers, and it is the reason a vocal skeptic reversed his position.
Ondrej is more optimistic than I expected on what breaks next
I asked what cracks first over the next 18 months, whether it is maintainer capacity or the CVE and CNA institutions. He acknowledged the pressure, citing roughly 47,000 CVEs in 2025 and a single recent month approaching 10,000, and he was direct that “these maintainers have no chance whatsoever to withstand the incoming load of new vulnerabilities that is just starting to come.” But he declined to predict a break, pointing instead at the wave of vendors and labs standing up open source support programs over the last several months. AISLE itself became a CNA under the ENISA route only a few weeks ago.
His read on the moment was my favorite line of the conversation. “If 2025 was mostly about naysayers, 2026 is people are realizing, my gosh, this actually works.” His advice to AppSec leaders followed from that, which was to stop leaning on legacy pattern matching tools when adversaries have unrestricted access to both frontier and open weight models, and to spend real time experimenting rather than waiting for the category to settle.
Thanks to Ondrej for coming on. Keep an eye on AISLE’s research at aisle.com/blog, since they are publishing some of the more useful empirical work on AI-driven vulnerability discovery right now.
