Insights on AI-powered product security: vulnerability triage, false-positive reduction, autonomous remediation, and closing the patch gap — from the team at Nullify.
You cannot tell an automated vulnerability repair worked unless you prove two things at once: the vulnerability is genuinely closed, and the program still works. Here is why the public AVR benchmarks cannot measure that, and how we built one that can.
A scanner finding is a guess. Nullify proves it: triaging down to what is real and reachable, then generating and running a reproducible exploit against a live target, with the evidence and a reproduction you can re-run yourself.
Nullify exists to automate security work end to end — find, triage, plan, fix — and to do it with AI you can actually control and measure, so a team's security outcomes stop scaling with its…
Prompting an agent to fix a vulnerability is a task, running a Product Security or Application Security program across hundreds of repos (or more) is a system, and the gap between them is the entire product.
Cyber didn't lose developers to laziness. It lost them to false positives. We'll show you the proof is in the data, and the way to rebuild the bridge of trust between cyber <> dev.
We are asked all the time about new model releases and security code review features from the big AI labs. This blog explains how Nullify is better together with these advancements to maximise the value they create for our customers.
