While researching CVE-2024-38428 in GNU’s Wget, our team found a new 0-day vulnerability. The vulnerability, later assigned CVE-2024-10524, may lead to various types of attacks – including phishing, SSRF, and MiTM. These attacks can have severe consequences such as resource restriction bypass and sensitive information exposure. Upon discovering this vulnerability, our team responsibly disclosed it to the Wget maintainers. A patch was released on November 11 and is included in Wget 1.25.0.

JFrog’s security research team continuously monitors open-source software registries, proactively identifying and addressing potential malware and vulnerability threats to foster a secure and reliable ecosystem for open-source software development and deployment.

Developers are expected to write new and more complex code to create leading-edge features in new software releases at a relenting pace. To do this they are looking for help from AI assistants like GitHub Copilot to help write better code, faster. They want to write, debug, and secure their code simultaneously, driving the need for leading-edge products like Copilot Autofix.

In the never-ending quest to speed up software release cycles, ensuring the security and integrity of application artifacts has never been more critical. As applications are continuously built, tested, and deployed, every element of the software pipeline—from source code to container images—needs to be trusted and verifiable. A key aspect of maintaining this trust is image integrity protection and validation.

On September 23rd, Twitter user Simone Margaritelli (@evilsocket) announced that he has discovered and privately disclosed a CVSS 9.9 GNU/Linux unauthenticated RCE, which affects almost all Linux distributions, and that the public disclosure will happen on September 30th, Due to a suspected leak in the disclosure process, @evilsocket decided to advance the disclosure, and on September 26th, the vulnerabilities were disclosed in @evilsocket’s blog, along with a full proof of concept.

At swampUP 2024 in Austin just a few days ago, we explored the EveryOps Matters approach with the crowd of developers, driven by a consolidated view from their companies’ boardrooms and 2024 CIO surveys. The message was clear: “EveryOps” isn’t just a strategy or tech trend — it’s a fundamental, ongoing mindset shift that must drive developers’ proactive actions in an ever-evolving software landscape. It’s not optional; it’s essential.

Picture this: You’ve just settled in at home after a long day, ready to relax, when suddenly your phone buzzes. It’s a notification about a failed build in your latest project. Your heart sinks. Your mind starts racing to connect the dots… What went wrong? Where is it broken? There’s usually no one immediately available to answer these questions, and you know it will require a large manual effort to get to the bottom of the issue.

When it comes to software supply chain security, we all do everything we can to prevent insecure software from being released into production. Hence we see software supply chain security shifting left to discover potential threats as early as possible in the software development lifecycle. But what happens when vulnerabilities are only discovered after an application has been distributed to its operating environment?

JFrog’s security research team continuously monitors open-source software registries, proactively identifying and addressing potential malware and vulnerability threats to foster a secure and reliable ecosystem for open-source software development and deployment. This blog details a PyPI supply chain attack technique the JFrog research team discovered had been recently exploited in the wild.

NOTE: This research was recently presented at Black Hat USA 2024, under the title “From MLOps to MLOops – Exposing the Attack Surface of Machine Learning Platforms”. The JFrog Security Research team recently dedicated its efforts to exploring the various attacks that could be mounted on open source machine learning (MLOps) platforms used inside organizational networks.