Anthropic's Claude Code Security: Game Changer or Hype?

General
1

Anthropic just launched Claude Code Security for automated vulnerability scanning. They’re claiming that Claude Opus 4.6 can find high-severity vulnerabilities that went undetected for decades.

I tested it on a client’s fintech codebase. Here are my unfiltered results.

What I Tested

Ran Claude Code Security on a 50K LOC Node.js/TypeScript codebase for a payment processing platform. This code has been through:

  • Manual security reviews
  • Snyk SAST scanning
  • CodeQL analysis
  • Two pentest rounds

So it’s been pretty thoroughly vetted.

The Results

Found: 3 real issues we hadn’t caught

  • A timing attack vulnerability in token comparison
  • A subtle race condition in account creation
  • An edge case in refund logic that could be exploited

False Positives: 12 issues flagged that weren’t actually vulnerabilities

  • Overly conservative about input validation
  • Flagged some intentional design patterns as risks
  • Didn’t understand business logic context

Missed: 1 known vulnerability from our pentest

  • A business logic flaw in the multi-factor auth flow
  • Requires understanding the entire authentication system

What It’s Good At

Claude Code Security excels at:

  • Finding logic flaws that pattern-based tools miss
  • Explaining vulnerabilities in clear language
  • Suggesting remediation with context
  • Cross-file analysis and data flow tracking

The timing attack it found was impressive - not an obvious pattern match, but actual reasoning about crypto implementation.

What It Struggles With

Same issues as other AI security tools:

  • Incomplete system context leads to overconfidence
  • Can’t threat model without understanding attacker motivations
  • Misses vulnerabilities that require business logic understanding
  • False positive rate higher than traditional SAST

My Take

Claude Code Security is a good first pass, not a replacement for security review.

Use it as:

  • First layer in defense-in-depth
  • Way to catch obvious and some non-obvious issues
  • Educational tool (explanations are excellent)

Don’t use it as:

  • Sole security gate
  • Replacement for threat modeling
  • Substitute for pentest/security review

Questions

Has anyone else tested this? What were your results? Especially interested in comparisons to Snyk, Semgrep, or other AI-enhanced SAST tools.

2

Sam, I tested this on our OAuth implementation flows specifically. Results align with yours.

Found: 2 issues

  • Token expiration edge case
  • Redirect URI validation flaw

Missed: Subtle timing attack similar to what you found, but in session management.

The tool is good but not comprehensive. I agree - first layer in defense-in-depth, not a replacement. How does it integrate with CI/CD? Is it fast enough for PR blocking, or better suited for scheduled scans?

3

This is interesting from a developer workflow perspective.

How fast is it? If it takes 10+ minutes to scan, it won’t work in our PR workflow. We need feedback within ~5 minutes or developers context-switch.

Also - cost at scale? You tested on 50K LOC. We’re at 500K+ LOC. Is this priced per scan, per line of code, per user?

And the false positive rate concerns me. 12 false positives out of 15 total findings means 80% noise. That’s rough on developer productivity if they have to investigate every flag.

4

From an enterprise perspective, the key question is: How does this fit into our existing security toolchain?

We already have Snyk, GitHub Advanced Security (CodeQL), and manual security reviews. Adding another tool means:

  • Training security team
  • Integrating into workflow
  • Deduplicating findings across tools
  • Justifying ROI to CFO

Sam, did you compare findings with Snyk and CodeQL? How much overlap vs unique findings?

The 3 real issues you found that other tools missed - that’s compelling. But need to understand if this is additive value or just a different tool finding different things with same overall coverage.

5

The timing attack finding is particularly interesting. That’s exactly the kind of subtle vulnerability that requires reasoning, not just pattern matching.

But Alex’s point about false positives is critical. If we’re adding security scanning to every PR, 80% false positive rate will create friction and alert fatigue.

Maybe the right approach is:

  • Use it for scheduled deep scans (weekly/monthly)
  • NOT for PR blocking
  • Security team triages findings
  • Real issues get backlogged and prioritized

That way you get the benefit of finding those subtle issues without slowing down development velocity.