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Deep dives into application security, tool comparisons, and industry insights. Learn from our security research and vulnerability analysis across open-source projects.

Control Drift: Why Your SOC 2 Compliance Can't Keep Up With AI Written Code

SOC 2complianceAI-generated codeSASTcontrol driftapplication security

When SAST tools generate 87% noise and miss the critical vulnerabilities entirely, your SOC 2 audit trail proves you ran a process - not that your code is secure. The gap between compliance paperwork and actual security is widening at AI speed, and the breaches have already started.

How We Got a 90% Fix Rate on Open Source Security Reports

vulnerability researchsemantic code analysisapplication securityopen source securityautomated remediation

Most automated security reports get ignored. We got 90.24% of ours accepted by doing what most tools skip: actually reading the code, understanding the architecture, and submitting fixes specific enough to merge without back-and-forth

Vibe Coding Is a Security Disaster That Is About to Happen

vibe codingapplication securityAI generated codesoftware vulnerabilitiesVibe Coding 1st Birthday

Millions of programmers send out code that they don't understand. 40% to 62% of it has security holes that can be used. The breaches have already begun.

The 87% Problem: Why Traditional Security Tools Generate Noise

SASTFalse PositivesAlert Fatigue Application SecuritySemantic Analysis

Traditional SAST tools have an 87% false positive rate - we proved it across 10 repositories and 1,183 findings. Meanwhile, real vulnerabilities slip through because pattern matching can't understand what code actually does.

The SQL Injection That SAST Didn't Find

NocoDBsemantic analysisapplication securitySQL injectionSAST comparison

Semgrep scanned NocoDB and flagged 222 issues but missed the critical SQL injection in the Oracle client - 17 injection points across one file, invisible to pattern matching because the code sat inside a query builder context. It's the clearest example of why semantic analysis catches what SAST can't.

Kolega.dev Team

The Hidden Risks of Modern Code: Security Patterns Modern Tools Still Miss

code securitymodern development securitysemantic analysissecurity tool comparisonautomated security validationKolega.dev

Development velocity has never been higher, yet security often trails behind. In every major project we assessed, we identified significant vulnerabilities that conventional security tools simply overlooked.

Why Most Security Alerts Are Noise (And How to Fix It)

SASTFalse PositivesAlert FatigueApplication SecurityDevSecOpsSemantic AnalysisSecurity Automation

Your SAST tool found 120 problems. Your team spent 20 hours sorting things out. You fixed 15 real problems. This is the problem of alert fatigue, but there is a way to fix it.

What We Found: 225 Vulnerabilities in 45 Open Source Projects

vulnerability researchsemantic code analysisapplication securityopen source securitySQL injectionrace conditionsauthentication bypassSAST comparisonKolega.dev

We used kolega.dev on 45 open source projects. These weren't just random GitHub repos; but mature projects, worked on and used by real users. Langfuse, Qdrant, NocoDB, Phase, Cloudreve, Agenta and Weaviate are all examples. We found 225 security holes in those 45 projects. So far, maintainers have reviewed 41 of our reports. Over 90% fix acceptance rate.

The 7 Best Code Security Solutions for 2026: Why Scanning the Old Way Isn't Enough

SAST tools 2026static analysis comparisonbest SAST solutionKolega.devSemgrep alternativeapplication securityvulnerability detectioncode security platform

Your SAST tool found 1,183 problems. There are only 153 real ones. There is a better way.

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