Your Code Scanners’ Blind Spot

 Are your code scanners leaving vulnerabilities undetected?

Our latest study, “Your Code Scanners’ Blind Spot – Measured,” reveals the limitations of traditional and AI-powered code security tools when used in isolation. By testing nine detector configurations across four paradigms—ranging from SAST tools to LLM-based detection—we discovered that comprehensive coverage requires a layered discovery approach rather than relying on a single scanner.

Key Insights from the Research:

1. Low Overlap Between Scanners: Out of 334 consolidated findings, only 14 were supported by multiple detectors. This highlights the importance of using a combination of tools, including traditional scanners, dataflow analysis, and AI code security scanners, to achieve complete vulnerability detection.
2. Critical Role of Existing Tools: Database-backed scanners like Trivy remain indispensable for identifying dependency-related vulnerabilities, ensuring compliance, and generating SBOMs. They effectively complement advanced methods without being replaced.
3. Validation and Remediation Bottlenecks: Detecting more vulnerabilities is only half the battle. Without an optimized pipeline for validating findings and fixing them, organizations risk being overwhelmed by noise. Our research emphasizes the importance of streamlining workflows to reduce false positives and improve remediation throughput.

This study underscores a vital shift in application security: moving beyond standalone code scanners to build layered, integrated detection systems that cover all vulnerability categories, from secrets scanners to complex architectural flaws.

Want to Learn More?

Download the full report to explore the research findings, real-world detector configurations, and practical strategies for strengthening your code security.