hunting-for-supply-chain-compromise

name	hunting-for-supply-chain-compromise
description	Hunt for supply chain compromise indicators including trojanized software updates, compromised dependencies, unauthorized code modifications, and tampered build artifacts. Use when hunting for supply chain compromise indicators including trojanized software updates,.
domain	cybersecurity
tags	["threat-hunting","mitre-attack","supply-chain","initial-access","t1195","proactive-detection"]
subdomain	threat-hunting
version	1.0
author	oyi77
license	Apache-2.0
d3fend_techniques	["Platform Hardening","Restore Object","Restore Software","Software Update","Asset Inventory"]
nist_csf	["DE.CM-01","DE.AE-02","DE.AE-07","ID.RA-05"]

Hunting For Supply Chain Compromise

Overview

Cybersecurity skill for hunting for supply chain compromise. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "hunting for supply chain compromise"

• "When proactively hunting for indicators of hunting for supply chain compromise i"

• "After threat intelligence indicates active campaigns using these techniques"

• "During incident response to scope compromise related to these techniques"

• When proactively hunting for indicators of hunting for supply chain compromise in the environment

• After threat intelligence indicates active campaigns using these techniques

• During incident response to scope compromise related to these techniques

• When EDR or SIEM alerts trigger on related indicators

• During periodic security assessments and purple team exercises

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

Prerequisites

• EDR platform with process and network telemetry (CrowdStrike, MDE, SentinelOne)
• SIEM with relevant log data ingested (Splunk, Elastic, Sentinel)
• Sysmon deployed with comprehensive configuration
• Windows Security Event Log forwarding enabled
• Threat intelligence feeds for IOC correlation

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text)  k, v  IOC_PATTERNS.items()}