Next-Gen SCA for C/C++: Closing the Detection Gap

Endor Labs has announced an update to its software composition analysis (SCA) product with new support for C and C++ applications, addressing the long-standing challenge these languages pose to traditional SCA tools. By leveraging a combination of artificial intelligence, deep code analysis, and an extensive index of open-source libraries, Endor Labs can now deliver precise risk insights and accurate remediation guidance for C and C++ codebases. This is achieved through a novel method of identifying code origins using "fingerprints" composed of cryptographic hashes and machine learning-generated code embeddings, which enable the detection of dependencies, even when code has been copied or modified. This approach significantly enhances the ability to detect vulnerabilities and track license compliance, yielding 81% fewer false negatives and 143% more true positives compared to existing tools, thus providing better visibility into potential risks for security and engineering teams.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.