In our previous blog post in this series we showed how the immaturity of the Machine Learning (ML) field allowed our team to discover and disclose 22 unique software vulnerabilities in ML-related projects, and we analyzed some of these vulnerabilities that allowed attackers to exploit various ML services.

Our stack to go from experimenting to production won't have any more secrets for you.

In what feels like 10 minutes, cybersecurity AI and machine learning (ML) have gone from a concept pioneered by a handful of companies, including SenseOn, to a technology that is seemingly everywhere. In a recent SenseOn survey, over 80% of IT teams told us they think that tools that use AI would be the most impactful investment their security operations centre (SOC) could make.

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.

False positives in API security are a serious problem, often resulting in wasted results and time, missing real threats, alert fatigue, and operational disruption. Fortunately, however, emerging technologies like machine learning (ML) can help organizations minimize false positives and streamline the protection of their APIs. Let's examine how.

App connectors are a critical component of the Netskope secure access service edge (SASE) platform, offering visibility into user activities based on their interactions with cloud applications. These connectors monitor various types of user actions, such as uploads, downloads, and sharing events in apps like Google Drive and Box, by analyzing network traffic patterns.

The momentous rise of AI continues, and more and more customers are demanding concrete results from these early implementations. The time has come for tech companies to prove what AI can do beyond adding conversational chat agents to website sidebars. Fortunately, it’s easy to see how cloud data protection has already benefited from advancements in AI and ML. Headline-grabbing large-language models are also making protecting data in the cloud easier to manage across organizations. ‍

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.

We made our WAF Machine Learning models 5.5x faster, reducing execution time by approximately 82%, from 1519 to 275 microseconds! Read on to find out how we achieved this remarkable improvement. WAF Attack Score is Cloudflare's machine learning (ML)-powered layer built on top of our Web Application Firewall (WAF). Its goal is to complement the WAF and detect attack bypasses that we haven't encountered before.