Hunting For Supply Chain Compromise

SkillMonitoring & ops

Lets your agent hunt security logs for signs that software updates or dependencies were tampered with.

Then ask your AI: use the Hunting For Supply Chain Compromise skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add it as a step in a pipeline you build on Ahel, or connect Ahel to Claude, ChatGPT, Cursor, Claude Code or Codex and ask your AI to use it.

About this skill

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, scop

What this skill tells your AI

The instructions your AI receives, as published by mukul975/anthropic-cybersecurity-skills in skills/hunting-for-supply-chain-compromise/SKILL.md and read by Ahel’s review.

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

ConceptDescription
T1195.001Compromise Software Dependencies
T1195.002Compromise Software Supply Chain
T1199Trusted Relationship

Tools & Systems

ToolPurpose
CrowdStrike FalconEDR telemetry and threat detection
Microsoft Defender for EndpointAdvanced hunting with KQL
Splunk EnterpriseSIEM log analysis with SPL queries
Elastic SecurityDetection rules and investigation timeline
SysmonDetailed Windows event monitoring
VelociraptorEndpoint artifact collection and hunting
Sigma RulesCross-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]

Signals

GitHub stars
34k
Forks
4k
Last commit
Aug 2026
Advanced
Item type
skill
Key
hunting-for-supply-chain-compromise
Source
github.com/mukul975/anthropic-cybersecurity-skills