IndiaBravo RomeoBravo

Hunt Hypothesis

This detection identifies adversary activity characterized by specific file signatures or behavioral patterns defined in the IndiaBravo RomeoBravo YARA rule within the Azure Sentinel environment. Proactive hunting for this indicator is essential to uncover early-stage threats that may not yet trigger high-severity alerts, allowing the SOC team to validate potential compromises before they escalate into significant incidents.

YARA Rule

rule IndiaBravo_RomeoBravo
{
	meta:
		copyright = "2015 Novetta Solutions"
		author = "Novetta Threat Research & Interdiction Group - [email protected]"
		Source = "6e3db4da27f12eaba005217eba7cd9133bc258c97fe44605d12e20a556775009"

	strings:
	/*
		E8 C3 FE FF FF     call    generate64ByteRandomNumber
		68 C8 01 00 00     push    1C8h            ; dwLength
		68 D8 E8 40 00     push    offset g_Config ; pvBuffer
		A3 80 EA 40 00     mov     dword ptr g_Config.qwIdentifier, eax
		89 15 84 EA 40 00  mov     dword ptr g_Config.qwIdentifier+4, edx
		E8 F9 E9 FF FF     call    DNSCALCDecode
		83 C4 08           add     esp, 8
		8D 4C 24 08        lea     ecx, [esp+214h+var_20C]
		6A 00              push    0
		51                 push    ecx
		68 C8 01 00 00     push    1C8h
		68 D8 E8 40 00     push    offset g_Config
		56                 push    esi
		FF 15 74 E7 40 00  call    WriteFile_9
		56                 push    esi
		FF 15 6C E7 40 00  call    CloseHandle_9
	*/

	$a = {E8 [4] 68 [2] 00 00 68 [4] A3 [4]	89 15 [4] E8 [4] 83 C4 08 8D [3] 6A 00 5? 68 [2] 00 00 	68 [4] 5? FF 15 [4] 5? 	FF 15}
		
		$b1 = "tmscompg.msi" wide
		$b2 = "cvrit000.bat"

	condition:
		2 of ($b*) or 
		$a in ((pe.sections[pe.section_index(".text")].raw_data_offset)..(pe.sections[pe.section_index(".text")].raw_data_offset + pe.sections[pe.section_index(".text")].raw_data_size))
}

Deployment Notes

This YARA rule can be deployed in the following contexts:

• Microsoft Defender for Endpoint — Custom indicators / advanced hunting
• Email Gateway — Attachment scanning
• File Share Monitoring — Periodic scanning of shared drives
• YARA CLI — Manual threat hunting on endpoints

This rule contains 3 string patterns in its detection logic.

• Source Rule

False Positive Guidance

Here are 4 specific false positive scenarios for the IndiaBravo RomeoBravo detection rule in an enterprise environment, along with recommended filters or exclusions:

• Antivirus Scheduled Scans on Development Servers

  • Scenario: The YARA rule triggers when the endpoint protection agent (e.g., CrowdStrike Falcon or Microsoft Defender for Endpoint) performs a deep heuristic scan on development servers. These scans often spawn temporary worker processes that load large memory modules, mimicking the behavior patterns defined in IndiaBravo_RomeoBravo.
  • Filter/Exclusion: Exclude alerts where the parent process is the AV service (e.g., MsMpEng.exe or Cortex.sys) and the event occurs during the configured maintenance window (e.g., 02:00 – 04:00 local time).

• Enterprise Patch Management Deployments

  • Scenario: During monthly patch cycles, tools like Microsoft Endpoint Configuration Manager (MECM/SCCM) or Ivanti execute installation scripts. These scripts frequently invoke PowerShell or batch files that load specific DLLs into memory, which the rule identifies as a potential anomaly due to the rapid sequence of file creation and process injection.
  • Filter/Exclusion: Create an exclusion for processes launched by known patching executables (e.g., ccmexec.exe, IvantiAgent.exe) and restrict the alert scope to exclude the specific directory paths used for staging patches (e.g., C:\Windows\CCMCache).

• Automated Log Rotation and Archiving Jobs

  • Scenario: A scheduled task managed by Task Scheduler or a third-party RMM tool like ManageEngine OpManager runs nightly to compress and archive application logs. This process involves opening large log files, writing temporary buffers, and executing compression