Hey, it’s Gio.

When we first started building Octane, one of the biggest questions we faced was this:

How do you make sense of 50,000+ bugs?

Because that’s what security tools surface when you scan across the smart contract ecosystem. Thousands of findings, patterns, and edge cases. Some matter. Most don’t.

The challenge isn’t just catching bugs, it’s prioritizing the ones that will actually impact your users.

That’s where clustering comes in.

What is Clustering?

Imagine dumping 50,000 marbles on the floor with each one a potential vulnerability. At first glance, it’s chaos.

But then you start grouping them by color.

You realize some share the same root cause: a denial-of-service loop, a missing access control, a rounding bug in Solidity.

Even if they were submitted by different developers, in different codebases, at different times they’re still related at their root. And if you fix one of them, you might just fix hundreds.

Sometimes, these clusters fall between known bug types. They’re not a perfect match for A or B, but share traits of both. In those cases, Octane forms a new cluster entirely, surfacing emerging vulnerability classes that don’t even have names yet.

This isn’t just visualization, it’s a decision-making tool.

Let’s say Cluster A represents 600 high-severity rounding bugs in Solidity. Cluster B is three obscure findings in a new Rust module. 

Clustering helps us decide what actually matters so your team can focus on the few clusters worth fixing, not every bug.

How Clusters Work Visually

Internally, we use a clustering visualization to help our security researchers and ML engineers spot patterns in the data.

Each dot in this image is a vulnerability.
Each color is a distinct cluster, usually sharing a root cause or vulnerability type.
Clusters close together in space? They’re more similar. Far apart? Fundamentally different issues.

Each cluster is built using semi-supervised learning with our models doing the grouping, then security experts step in to assess which ones are worth prioritizing. This hybrid model keeps the system scalable, without losing the judgment that comes from real-world experience.

This visualization helps us understand the shape of the ecosystem. It shows us what bugs are trending, what new exploit classes are emerging, and how to prioritize model improvements.

How Octane Uses Clustering

Octane’s internal clustering engine helps our team make sense of massive data sets, especially during onboarding or when scanning large codebases.

It allows us to filter:

• By severity: Filter to only high or critical vulnerabilities.

• By language: Isolate findings in Solidity, Rust, TypeScript, etc.

• By root cause or parent type: Focus on bugs tied to access control, rounding, reentrancy, or whatever’s most urgent.

• By project context: Zoom into just your repo or compare trends across similar protocols.

The system doesn’t just show us what’s wrong, it helps us understand why. It surfaces patterns across thousands of seemingly unrelated issues and gives us the reasoning to act. Think of it like turning thousands of raw bug reports into five clear action plans.

Why It Matters

By prioritizing clusters that show up often, affect multiple customers, and share a common root cause, we can fix more with less effort.

Clustering helps us:

• Make smarter decisions, faster

• Spot patterns across thousands of findings

• Reduce time wasted chasing edge cases or false positives

• Focus remediation efforts where they’ll have the biggest impact

In practice, this often means addressing five clusters and resolving 60% of vulnerabilities in one shot. That’s the kind of leverage traditional audits rarely provide.

And once we fix a root cause at the cluster level, Octane tracks how many downstream findings were resolved automatically. That allows us to see the ripple effect of our fixes in real time.

50,000 bugs used to mean 50,000 decisions. Now it means five.

If you’re drowning in low-priority alerts or spending hours triaging bugs that don’t matter, you’re not alone.

We wrote a full breakdown on how Octane uses unsupervised ML to surface the real threats hiding in your stack and how clustering helps you fix more with less.

More soon,
Gio

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