Author: Jelle Vergeer This blog will be a technical deep-dive into CyberArk credential files and how the credentials stored in these files are encrypted and decrypted. I discovered it was possible to reverse engineer the encryption and key generation algorithms and decrypt the encrypted vault password. I also provide a python implementation to decrypt the … Continue reading Reverse engineering and decrypting CyberArk vault credential files
Category: Uncategorized
TA505: A Brief History Of Their Time
Threat Intel Analyst: Antonis Terefos (@Tera0017)Data Scientist: Anne Postma (@A_Postma) 1. Introduction TA505 is a sophisticated and innovative threat actor, with plenty of cybercrime experience, that engages in targeted attacks across multiple sectors and geographies for financial gain. Over time, TA505 evolved from a lesser partner to a mature, self-subsisting and versatile crime operation with … Continue reading TA505: A Brief History Of Their Time
Decrypting OpenSSH sessions for fun and profit
Author: Jelle Vergeer Introduction A while ago we had a forensics case in which a Linux server was compromised and a modified OpenSSH binary was loaded into the memory of a webserver. The modified OpenSSH binary was used as a backdoor to the system for the attackers. The customer had pcaps and a hypervisor snapshot … Continue reading Decrypting OpenSSH sessions for fun and profit
StreamDivert: Relaying (specific) network connections
Author: Jelle Vergeer The first part of this blog will be the story of how this tool found its way into existence, the problems we faced and the thought process followed. The second part will be a more technical deep dive into the tool itself, how to use it, and how it works. Storytime About … Continue reading StreamDivert: Relaying (specific) network connections
Machine learning from idea to reality: a PowerShell case study
Detecting both ‘offensive’ and obfuscated PowerShell scripts in Splunk using Windows Event Log 4104 Author: Joost Jansen This blog provides a ‘look behind the scenes’ at the RIFT Data Science team and describes the process of moving from the need or an idea for research towards models that can be used in practice. More specifically, … Continue reading Machine learning from idea to reality: a PowerShell case study
A Second Look at CVE-2019-19781 (Citrix NetScaler / ADC)
Authors: Rich Warren of NCC Group FSAS & Yun Zheng Hu of Fox-IT, in close collaboration with Fox-IT’s RIFT. About the Research and Intelligence Fusion Team (RIFT): RIFT leverages our strategic analysis, data science, and threat hunting capabilities to create actionable threat intelligence, ranging from IOCs and detection capabilities to strategic reports on tomorrow’s threat … Continue reading A Second Look at CVE-2019-19781 (Citrix NetScaler / ADC)
LDAPFragger: Command and Control over LDAP attributes
Written by Rindert Kramer Introduction A while back during a penetration test of an internal network, we encountered physically segmented networks. These networks contained workstations joined to the same Active Directory domain, however only one network segment could connect to the internet. To control workstations in both segments remotely with Cobalt Strike, we built a … Continue reading LDAPFragger: Command and Control over LDAP attributes
Hunting for beacons
Author: Ruud van Luijk Attacks need to have a form of communication with their victim machines, also known as Command and Control (C2) [1]. This can be in the form of a continuous connection or connect the victim machine directly. However, it’s convenient to have the victim machine connect to you. In other words: It … Continue reading Hunting for beacons
Detecting random filenames using (un)supervised machine learning
Combining both n-grams and random forest models to detect malicious activity. Author: Haroen Bashir An essential part of Managed Detection and Response at Fox-IT is the Security Operations Center. This is our frontline for detecting and analyzing possible threats. Our Security Operations Center brings together the best in human and machine analysis and we continually … Continue reading Detecting random filenames using (un)supervised machine learning
mkYARA – Writing YARA rules for the lazy analyst
Writing YARA rules based on executable code within malware can be a tedious task. An analyst cannot simply copy and paste raw executable code into a YARA rule, because this code contains variable values, such as memory addresses and offsets. The analyst has to disassemble the code and wildcard all the pieces in the code … Continue reading mkYARA – Writing YARA rules for the lazy analyst