The Axios npm supply chain incident: fake dependency, real backdoor

On March 31, 2026, two malicious Axios versions (1.14.1 and 0.30.4) were briefly published to npm via a compromised maintainer account. The only change performed was the addition of a trojanized dependency, whose postinstall script deployed a cross‑platform RAT (for macOS, Windows, and Linux). Although the Axios packages were removed within hours, multiple hits were … Continue reading The Axios npm supply chain incident: fake dependency, real backdoor →

Capture the Kerberos Flag: Detecting Kerberos Anomalies

Kerberos is one of the most common protocols in organizations that utilize Windows Active Directory, and an essential part of Windows authentication used to verify the identity of a user or a host [1]. As such, Kerberos is often a target for adversaries trying to either steal or forge Kerberos tickets [2]. In this blog … Continue reading Capture the Kerberos Flag: Detecting Kerberos Anomalies →

ConsentFix (a.k.a. AuthCodeFix): Detecting OAuth2 Authorization Code Phishing

ConsentFix (a.k.a.AuthCodeFix) is the latest variant of the fix-type phishing attacks, initially identified by Push Security. In this technique, the adversary tricks the victim into generating an OAuth authorization code that is part of a localhost URL, by signing in to the Azure CLI instance (or other vulnerable applications). Then, the victim is instructed to copy that URL and paste it into a phishing website, essentially handing over the authorization code to the adversary, who is now able to exchange it for an access token. Using the access token, the adversary gets access to the victim's Microsoft account.

Detection Engineering: Practicing Detection-as-Code – Tuning – Part 8

In Part 7, we showcased how we can leverage automation to continuously monitor the performance and trigger rate of our deployed detections. In this part, we are going to investigate how we can introduce automation and utilize continuous deployment pipelines to streamline the tedious task of tuning our detections.

Detection Engineering: Practicing Detection-as-Code – Monitoring – Part 7

In this part, we are going to introduce automation to effectively monitor our deployed detections. By setting up automations at this phase we adopt a proactive approach towards maintenance, allowing our team to take action before a blowout of alerts or an untuned detection is escalated by the SOC.

Detection Engineering: Practicing Detection-as-Code – Deployment – Part 6

The deployment phase is one of the most challenging steps in the Detection Development Life Cycle due to its implementation complexity. In this part, we will explore the principles and practices of deploying rules to target platforms. Additionally, we will go through some of the challenges encountered when designing and implementing a deployment pipeline, along with suggestions on how to overcome them, to ensure that our Continuous Deployment pipeline operates smoothly.