When looking for sensitive information and other valuable assets, attackers rarely access their target directly. Instead, they find vulnerabilities in other components and use them to weave through the system and escalate privileges where they can. Because containers add a layer of complexity to already large and complex applications, the attack surface is increased, giving threat actors more to work with.

Just a few weeks ago, we wrote about how the National Vulnerability Database (NVD) is seriously behind in enriching CVEs. On LinkedIn, Mastodon, and other social sites, the NVD’s mounting backlog and what should be done about it has become a hot topic of conversation within the cybersecurity community.

Published in 2023, the OWASP Top 10 for LLM Applications is a monumental effort made possible by a large number of experts in the fields of AI, cybersecurity, cloud technology, and beyond. OWASP contributors came up with over 40 distinct threats and then voted and refined their list down to the ten most important vulnerabilities.

The risk both to and from AI models is a topic so hot it’s left the confines of security conferences and now dominates the headlines of major news sites. Indeed, the deluge of frightening hypotheticals can make AI feel like we are navigating an entirely new frontier with no compass. And to be sure, AI poses a lot of unique challenges to security, but remember: Both the media and AI companies have a vested interest in upping the fright hype to keep people talking.

*April 1 update. it was confirmed that Fedora 40 is not affected by the backdoor. However, users should still downgrade to a 5.4 build to be safe. On March 29th, 2024, a critical CVE was issued for the XZ-Utils library. This vulnerability allows an attacker to run arbitrary code remotely on affected systems. Due to its immediate impact and wide scope, the vulnerability has scored 10 for both CVSS 3.1 and CVSS 4, which is the highest score available.

Early on March 28, 2024, the Mend.io research team detected more than 100 malicious packages targeting the most popular machine learning (ML) libraries from the PyPi registry. Among those libraries are Pytorch, Matplotlib, and Selenium.

Containers have taken over the world of software development. According to Gartner analysts, “90% of global organizations will be running containerized applications in production by 2026,” up from 40% in 2021. Containerized applications provide enterprises with an agile, modern approach in the age of cloud computing; safeguarding these technologies from existing and future threats requires equally modern methods.

While cloud-native development brilliantly solves problems related to scalability and effective resource use, a more complex architecture and new security challenges come along for the ride as well. The added layer of abstraction of container architecture can make tracking down vulnerabilities and poorly stored secrets, assessing true risk, and enforcing policies difficult for security teams using only traditional AppSec tools.

Headed by NIST, an American government institution, the National Vulnerability Database (NVD) contains vulnerability data that’s been key to protecting organizations both within and without the US borders for more than 20 years. Many security policies from both commercial and government organizations require that vendors take care of vulnerabilities of a particular severity as given by the NVD within a certain number of days.

Securing AI is a top cybersecurity priority and concern for governments and businesses alike. Developers have easy access to pre-trained AI models through platforms like Hugging Face and to AI-generated functions and programs through large language models (LLMs) like GitHub Co-Pilot. This access has spurred developers to create innovative software at an enormously fast pace.