Making frontier cybersecurity capabilities available to defenders

Not super surprising that Anthropic is shipping a vulnerability detection feature -- OpenAI announced Aardvark back in October (https://openai.com/index/introducing-aardvark/) and Google announced BigSleep in Nov 2024 (https://cloud.google.com/blog/products/identity-security/clo...).

The impact question is really around scale; a few weeks ago Anthropic claimed 500 "high-severity" vulnerabilities discovered by Opus 4.6 (https://red.anthropic.com/2026/zero-days/). There's been some skepticism about whether they are truly high severity, but it's a much larger number than what BigSleep found (~20) and Aardvark hasn't released public numbers.

As someone who founded a company in the space (Semgrep), I really appreciated that the DARPA AIxCC competition required players using LLMs for vulnerability discovery to disclose $cost/vuln and the confusion matrix of false positives along with it. It's clear that LLMs are super valuable for vulnerability discovery, but without that information it's difficult to know which foundation model is really leading.

What we've found is that giving LLM security agents access to good tools (Semgrep, CodeQL, etc.) makes them significantly better esp. when it comes to false positives. We think the future is more "virtual security engineer" agents using tools with humans acting as the appsec manager. Would be very interested to hear from other people on HN who have been trying this approach!

Anakin: I'm going to save the world with my AI vulnerability scanner, Padme.

Padme: You're scanning for vulnerabilities so you can fix them, Anakin?

Anakin: ...

Padme: You're scanning for vulnerabilities so you can FIX THEM, right, Annie?

FWIW Claude Code Opus 4.5 ranks ~71% accuracy on the OpenSSF CVE Benchmark that we ran against DeepSource (https://deepsource.com/benchmarks).

We have a different approach, in that we're using SAST as a fast first pass on the code (also helps ground the agent, more effective than just asking the model to "act like a security researcher"). Then, we're using pre-computer static analysis artifacts about the code (like data flow graphs, control flow graphs, dependency graphs, taint sources/sinks) as "data sources" accessible to the agent when the LLM review kicks in. As a result, we're seeing higher accuracy than others.

Haven't gotten access to this new feature yet, but when we do we'd update our benchmarks.

> "Rather than scanning for known patterns, Claude Code Security reads and reasons about your code the way a human security researcher would: understanding how components interact, tracing how data moves through your application, and catching complex vulnerabilities that rule-based tools miss."

Fascinating! Our team has been blending static code analysis and AI for a while and think it's a clever approach for the security use case the Anthropic team's targeting here.

I hope this is better than their competitors products. So far I've been underwhelmed. They basically just find stuff that's already identified by static analysis tooling and toss in a bunch of false positives from the AI scans.

I would love to know how this compares to just prompting Claude Code with "please find and fix any security vulnerabilities in this code"

There's a lot of skepticism in the security world about whether AI agents can "think outside the box" enough to replicate or augment senior-level security engineers.

I don't yet have access to Claude Code Security, but I think that line of reasoning misses the point. Maybe even the real benefit.

Just like architectural thinking is still important when developing software with AI, creative security assessments will probably always be a key component of security evaluation.

But you don't need highly paid security engineers to tell you that you forgot to sanitize input, or you're using a vulnerable component, or to identify any of the myriad issues we currently use "dumb" scanners for.

My hope is that tools like this can help automate away the "busywork" of security. We'll see how well it really works.

Limited preview for researchers, who will be hand picked to write positive reviews.

Enough of this frontier grifting. Make it testable for open source developers at no cost and without login or get lost. You won't of course, because you'd get an unfiltered evaluation instead of guerilla marketing via blog posts, secrecy, and name-dropping researchers that cannot be disclosed.

I thought they'd noticed how many of my Claude tokens I've been burning trying to build defences against the AI bot swarms. Sadly not.

Solve a problem and everyone praises you.

No one knows you also caused that problem.