This hypothesis posits that adversaries are executing initial reconnaissance or lateral movement activities characterized by the specific behavioral patterns defined in the Sakula v1.1 rule. Proactively hunting for these indicators within Azure Sentinel is essential to identify subtle, low-severity anomalies before they escalate into significant security incidents.
rule sakula_v1_1: RAT
{
meta:
description = "Sakula v1.1"
date = "2015-10-13"
author = "Airbus Defence and Space Cybersecurity CSIRT - Yoann Francou"
strings:
$m1 = "%d_of_%d_for_%s_on_%s"
$m2 = "/c ping 127.0.0.1 & del /q \"%s\""
$m3 = "=%s&type=%d"
$m4 = "?photoid="
$m5 = "iexplorer"
$m6 = "net start \"%s\""
$v1_1 = "MicroPlayerUpdate.exe"
$MZ = "MZ"
condition:
$MZ at 0 and all of them
}
This YARA rule can be deployed in the following contexts:
This rule contains 8 string patterns in its detection logic.
Here are 5 specific false positive scenarios for the Sakula v1.1 detection rule in an enterprise environment, including suggested filters and exclusions:
Patch Management Deployment via SCCM/MECM
C:\Windows\CCM\ where the parent process is ccmexec.exe. Additionally, filter out events occurring strictly between 08:00 and 12:00 UTC on Tuesdays and Thursdays (standard patch windows).Automated Backup Jobs via Veeam Agent
VeeamAgent.exe or vbragent.exe. Ensure the exclusion applies specifically when the command line arguments contain keywords like -backup, -snapshot, or -fullscan.Microsoft Defender Antivirus Real-Time Scanning
C:\Program Files\Windows Defender\MsMpEng.exe or MsMpRes.dll. Add a condition to ignore these events if the user context is