The hypothesis is that the detection identifies potential Turla malware activity associated with the RUAG APT group, leveraging specific indicators observed in the malware sample. SOC teams should proactively hunt for this behavior in Azure Sentinel to identify and mitigate early-stage Turla attacks that may evade traditional detection methods.
YARA Rule
rule Turla_APT_Malware_Gen1
{
meta:
description = "Detects Turla malware (based on sample used in the RUAG APT case)"
author = "Florian Roth"
family = "Turla"
reference = "https://www.govcert.admin.ch/blog/22/technical-report-about-the-ruag-espionage-case"
date = "2016-06-09"
hash1 = "0e1bf347c37fb199886f1e675e372ba55ac4627e8be2f05a76c2c64f9b6ed0e4"
hash2 = "7206075cd8f1004e8f1f759d46e98bfad4098b8642412811a214c0155a1f08b9"
hash3 = "fe3ffd7438c0d38484bf02a78a19ea81a6f51b4b3f2b2228bd21974c2538bbcd"
hash4 = "c49111af049dd9746c6b1980db6e150b2a79ca1569b23ed2cba81c85c00d82b4"
hash5 = "b62a643c96e2e41f639d2a8ce11d61e6b9d7fb3a9baf011120b7fec1b4ee3cf4"
hash6 = "edb12790b5cd959bc2e53a4b369a4fd747153e6c9d50f6a69ff047f7857a4348"
hash7 = "8f2ea0f916fda1dfb771f5441e919c561da5b6334b9f2fffcbf53db14063b24a"
hash8 = "8dddc744bbfcf215346c812aa569e49523996f73a1f22fe4e688084ce1225b98"
hash9 = "0c69258adcc97632b729e55664c22cd942812336d41e8ea0cff9ddcafaded20f"
hash10 = "2b4fba1ef06f85d1395945db40a9f2c3b3ed81b56fb9c2d5e5bb693c230215e2"
strings:
$x1 = "too long data for this type of transport" fullword ascii
$x2 = "not enough server resources to complete operation" fullword ascii
$x3 = "Task not execute. Arg file failed." fullword ascii
$x4 = "Global\\MSCTF.Shared.MUTEX.ZRX" fullword ascii
$s1 = "peer has closed the connection" fullword ascii
$s2 = "tcpdump.exe" fullword ascii
$s3 = "windump.exe" fullword ascii
$s4 = "dsniff.exe" fullword ascii
$s5 = "wireshark.exe" fullword ascii
$s6 = "ethereal.exe" fullword ascii
$s7 = "snoop.exe" fullword ascii
$s8 = "ettercap.exe" fullword ascii
$s9 = "miniport.dat" fullword ascii
$s10 = "net_password=%s" fullword ascii
condition:
( uint16(0) == 0x5a4d and filesize < 2000KB and ( 2 of ($x*) or 8 of ($s*) ) ) or ( 12 of them )
}This YARA rule can be deployed in the following contexts:
This rule contains 14 string patterns in its detection logic.
Scenario: Legitimate system update using Turla-related tools
Description: A system update or patching process may include tools or scripts that share similar hashes or behaviors with Turla malware.
Filter/Exclusion: Exclude processes initiated by known patch management tools like Microsoft Update or WSUS, or filter by ProcessName like wusa.exe or msiexec.exe.
Scenario: Scheduled job for log analysis using Turla-like scripts
Description: A scheduled job might run a script that performs log analysis or data exfiltration, mimicking Turla’s behavior.
Filter/Exclusion: Exclude processes associated with Splunk, ELK Stack, or SIEM tools running under a known admin account used for log analysis.
Scenario: Admin task involving PowerShell remoting or script execution
Description: An administrator may use PowerShell to execute remote commands or scripts, which could trigger the Turla detection rule.
Filter/Exclusion: Exclude processes with ProcessName like powershell.exe where the command line includes known admin tools like Invoke-Command or Enter-PSSession.
Scenario: Use of legitimate encryption tools with similar behavior
Description: Tools like GPG or OpenSSL may be used for encryption, which could resemble Turla’s data exfiltration or encryption activities.
Filter/Exclusion: Exclude processes with ProcessName like gpg.exe or openssl.exe and filter by command-line arguments related to encryption or key management.
Scenario: False positive from a third-party security tool or sandbox
Description: A security tool or sandbox environment may generate false positives by executing Turla-like samples during analysis.
Filter/Exclusion: Exclude processes originating from sandbox environments