Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Machine Learning

ALPHV Blackcat, GCP-Native Attacks, Bandook RAT, NoaBot Miner, Ivanti Secure Vulnerabilities, and More: Hacker's Playbook Threat Coverage Round-up: February 2024

In this version of the Hacker’s Playbook Threat Coverage round-up, we are highlighting attack coverage for newly discovered or analyzed threats, including those based on original research conducted by SafeBreach Labs. SafeBreach customers can select and run these attacks and more from the SafeBreach Hacker’s Playbook™ to ensure coverage against these advanced threats.

Data Scientists Targeted by Malicious Hugging Face ML Models with Silent Backdoor

In the realm of AI collaboration, Hugging Face reigns supreme. But could it be the target of model-based attacks? Recent JFrog findings suggest a concerning possibility, prompting a closer look at the platform’s security and signaling a new era of caution in AI research. The discussion on AI Machine Language (ML) models security is still not widespread enough, and this blog post aims to broaden the conversation around the topic.

The DevSecOps Hangout

Curious to see what all the AI/ML hype is about? Watch our DevSecOps Hangout and hear how ML Model management benefits organizations by providing a single place to manage ALL software binaries, bringing DevOps best practices to ML development, and allowing organizations to ensure the integrity and security of ML models – all while leveraging an existing solution they already have in place. Watch our expert educational talks and panel discussion with our Technology Partner Qwak on MLOps, DevSecOps, AI, and Machine Learning.

Future of VPNs in Network Security for Workers

The landscape of network security is continuously evolving, and Virtual Private Networks (VPNs) are at the forefront of this change, especially in the context of worker security. As remote work becomes more prevalent and cyber threats more sophisticated, the role of VPNs in ensuring secure and private online activities for workers is more crucial than ever. Let's explore the anticipated advancements and trends in VPN technology that could redefine network security for workers.

Integrating JFrog Artifactory with Amazon SageMaker

Today, we’re excited to announce a new integration with Amazon SageMaker! SageMaker helps companies build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. By leveraging JFrog Artifactory and Amazon SageMaker together, ML models can be delivered alongside all other software development components in a modern DevSecOps workflow, making each model immutable, traceable, secure, and validated as it matures for release.

Evolving ML Model Versioning

TL;DR: JFrog’s ML Model Management capabilities, which help bridge the gap between AI/ML model development and DevSecOps, are now Generally Available and come with a new approach to versioning models that benefit Data Scientists and DevOps Engineers alike. Model versioning can be a frustrating process with many considerations when taking models from Data Science to Production.

Understanding precision, recall, and false discovery in machine learning models

There are various ways to measure any given machine learning (ML) model’s ability to produce correct predictions, depending on the task that the system performs. Named Entity Recognition (NER) is one such task, in which a model identifies spans of sensitive data within a document. Nightfall uses NER models extensively to detect sensitive data across cloud apps like Slack, Microsoft Teams, GitHub, Jira, ChatGPT, and more.

Cato Application Catalog - How we supercharged application categorization with AI/ML

New applications emerge at an almost impossible to keep-up-with pace, creating a constant challenge and blind spot for IT and security teams in the form of Shadow IT. Organizations must keep up by using tools that are automatically updated with latest developments and changes in the applications landscape to maintain proper security. An integral part of any SASE product is its ability to accurately categorize and map user traffic to the actual application being used.