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Get key insights from the 2024 Dependency Management webinar with Darren Meyer and Henrik Plate. We discuss how to prioritize vulnerabilities, navigate breaking changes, and leverage public vulnerability databases effectively.
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This blog covers key steps to simplify FedRAMP vulnerability management, helping you reduce risks and meet compliance timelines. It also provides practical tips to empower developers and streamline fixes for a smoother FedRAMP process.
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GitHub Actions are open source dependencies - secure them accordingly! Learn how to effectively manage the security risks associated with GitHub Actions with a proactive approach focusing on three key areas: visibility, hardening, and dependency management.
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Explore the five key categories of reachability and their practical applications in AppSec and development. Learn the differences between SCA and container scanning, and understand how various tools like Function-Level Reachability, Package Baselining, and Internet Reachability play crucial roles in identifying and prioritizing security risks.
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Explore the challenges of modern vulnerability management and the efficiency of the Vulnerability Exploitability eXchange (VEX) in our latest blog post. Learn how VEX helps identify and communicate the true exploitability of vulnerabilities, streamlining cybersecurity efforts in the face of overwhelming scanner findings.
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If you’ve been watching the software supply chain security space evolve, you likely know that a lot of the momentum and effort is coming out of the U.S. Federal government. This may seem surprising at first, but it shouldn’t be, when you account for the fact that the Federal government is one of the single largest procurers of technology and software in the world.
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Phantom dependencies are dependencies used by your code that are not declared in the manifest. If you miss them, they can sneak reachable risks into your application, lead to false positives, or inaccurate SBOMs. All very spooky. This article breaks down how phantom dependencies happen, and how to catch them.
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SINET, an organization with the mission to accelerate Cybersecurity innovation through public-private partnerships, announced today that Endor Labs is one of the winners of its annual SINET16 Innovator Award. Endor Labs and 15 other emerging companies are identified as the most innovative and compelling technologies in their fields to address Cybersecurity threats and vulnerabilities.
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Exploit Prediction Scoring Systems (EPSS) is a data set that helps you understand the likelihood that a CVE will be exploited. Learn what the EPSS includes and how to use it to prioritize vulnerability remediation.
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What’s the best of the best when it comes to open source security tools?We’ve previously talked about the OpenSSF Scorecard, which gives developers a high-level snapshot of the security of any given open source project. But in this post, we’ll talk about a related project, the Open Source Security Index (OSSI), which does something slightly different and complementary.
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As projects grow larger and more complex, developers face challenges in maintaining a clean and efficient development workflow. Fortunately, npm workspaces offer an essential solution to streamline JavaScript development. In this blog post, we will explore the concept of npm/yarn workspaces, its importance, and how Endor Labs works with them.
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Join us at OWASP MSP October Meetup!
We’re excited to feature Darren Meyer, Staff Research Engineer at Endor Labs, at the upcoming OWASP MSP October Meetup, who will present an insightful session titled “What’s in Your AI Code? Learn Why Every SCA Tool is Wrong, and How to Deal with It.”
Session Overview:
With the rise of AI-fueled by Python-based libraries, it has become of paramount importance to scan Python-based projects and their dependencies for OSS vulnerabilities. Python relies on package managers like pip or conda to manage declared dependencies. Dependencies are declared in manifest files which the package manager uses to install the correct version of the required dependency. However, Python’s dependency management system coupled with its dynamic type nature makes it an especially challenging language to deal with.
Of particular focus is the phenomenon of phantom dependencies which are unreported dependencies in a project's manifest profile. These hidden dependencies, which are often provided dependencies (which is especially true for libraries such as tensorflow and pytorch which are essential for AI), challenge software composition analysis (SCA) of Python code, impacting the reliability of vulnerability results.
Join us to learn how to navigate these challenges and enhance your understanding of securing AI applications against unseen threats.
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