Anthropic-Cybersecurity-Skills/skills/hunting-for-supply-chain-compromise/SKILL.md

name, description, domain, subdomain, tags, version, author, license, d3fend_techniques, nist_csf, mitre_attack

name	description	domain	subdomain	tags	version	author	license	d3fend_techniques	nist_csf	mitre_attack
hunting-for-supply-chain-compromise	Runs a hypothesis-driven threat hunt for supply-chain compromise (T1195) by querying SIEM/EDR logs for trojanized software updates, compromised dependencies, unauthorized code modifications, and tampered build artifacts. Use when hunting after threat intel flags a compromised vendor/dependency, scoping a build-pipeline compromise, or reviewing update/build integrity.	cybersecurity	threat-hunting	threat-hunting mitre-attack supply-chain initial-access t1195 proactive-detection	1.0	mahipal	Apache-2.0	Platform Hardening Restore Object Restore Software Software Update Asset Inventory	DE.CM-01 DE.AE-02 DE.AE-07 ID.RA-05	T1046 T1057 T1082 T1083 T1195

Hunting For Supply Chain Compromise

When to Use

• 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

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

1. Formulate Hypothesis: Define a testable hypothesis based on threat intelligence or ATT&CK gap analysis.
2. Identify Data Sources: Determine which logs and telemetry are needed to validate or refute the hypothesis.
3. Execute Queries: Run detection queries against SIEM and EDR platforms to collect relevant events.
4. Analyze Results: Examine query results for anomalies, correlating across multiple data sources.
5. Validate Findings: Distinguish true positives from false positives through contextual analysis.
6. Correlate Activity: Link findings to broader attack chains and threat actor TTPs.
7. Document and Report: Record findings, update detection rules, and recommend response actions.

Key Concepts

Concept	Description
T1195.001	Compromise Software Dependencies
T1195.002	Compromise Software Supply Chain
T1199	Trusted Relationship

Tools & Systems

Tool	Purpose
CrowdStrike Falcon	EDR telemetry and threat detection
Microsoft Defender for Endpoint	Advanced hunting with KQL
Splunk Enterprise	SIEM log analysis with SPL queries
Elastic Security	Detection rules and investigation timeline
Sysmon	Detailed Windows event monitoring
Velociraptor	Endpoint artifact collection and hunting
Sigma Rules	Cross-platform detection rule format

Common Scenarios

1. Scenario 1: SolarWinds-style update mechanism compromise
2. Scenario 2: Compromised npm/PyPI package with backdoor
3. Scenario 3: Tampered build server deploying malicious artifacts
4. Scenario 4: Vendor VPN software update delivering malware

Output Format

Hunt ID: TH-HUNTIN-[DATE]-[SEQ]
Technique: T1195.001
Host: [Hostname]
User: [Account context]
Evidence: [Log entries, process trees, network data]
Risk Level: [Critical/High/Medium/Low]
Confidence: [High/Medium/Low]
Recommended Action: [Containment, investigation, monitoring]