CleanStart vs. Binarly

CleanStart offers a comprehensive solution for ensuring secure container images and fortifying software supply chain security, equipping organizations with lightweight and resilient base images free from vulnerabilities, which serve as a dependable foundation for the development, deployment, and operation of modern software while promoting enhanced safety and adherence to regulatory standards. By steering clear of generic distributions that frequently contain known vulnerabilities, CleanStart delivers images with near-zero CVEs, which greatly minimizes the attack surface by removing unnecessary components and embedding security features from the beginning, thus speeding up release cycles and alleviating the challenges of ongoing patching and remediation. Each image provided by CleanStart is subject to ongoing validation through signed attestations and Software Bill of Materials (SBOMs) that outline the origins of components, their provenance, and details about the build environment, thereby offering teams cryptographically verifiable evidence of their container contents, which is vital for auditing, compliance, and effective risk management. In addition, this method not only safeguards the software supply chain but also encourages a culture of accountability and transparency within the organization, ultimately leading to greater trust and collaboration among teams. Such an approach ensures that security is embedded into the software development lifecycle from the outset, fostering an environment where security best practices are prioritized and maintained.

Identify and tackle both established and emerging vulnerabilities across the entire spectrum of the device and software supply chain. Our approach goes beyond merely matching binaries to a list of known vulnerabilities; we strive to gain a deeper understanding of code execution, enabling us to detect flaws that exist beyond the binary surface. By employing this methodology, we can identify broad categories of defects that extend beyond just known issues, doing so quickly and with an impressive accuracy rate that minimizes false positives. Our emphasis lies in uncovering both familiar and novel vulnerabilities, along with any potentially harmful activities, rather than depending solely on hash or signature evaluations. Our analysis encompasses more than just CVEs, allowing us to pinpoint vulnerabilities that can be found at the binary level. Moreover, by utilizing machine learning techniques, we significantly reduce alert fatigue, achieving a nearly flawless rate of false positives while simultaneously improving our detection prowess. This holistic strategy not only fortifies our defenses but also ensures that we stay ahead of a diverse array of security threats, enabling us to adapt and respond effectively to the ever-evolving landscape of cybersecurity challenges.