threat hunting

Hunting For Supply Chain Compromise

Hunt for supply chain compromise indicators including trojanized software updates, compromised dependencies, unauthorized code modifications, and tampered build artifacts.

initial-accessmitre-attackproactive-detectionsupply-chaint1195threat-hunting
Install this skill
npx skills add mukul975/Anthropic-Cybersecurity-Skills
Framework mappings

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]
Source materials

References and resources

Everything below is rendered for inspection. Script files are read-only and never run.

References 3

api-reference.md1.9 KB

API Reference: Hunting for Supply Chain Compromise

NPM Lock File Analysis

import json
data = json.load(open("package-lock.json"))
packages = data.get("packages", {})
for name, info in packages.items():
    resolved = info.get("resolved", "")
    has_script = info.get("hasInstallScript", False)

pip-audit

pip-audit --format=json --output=audit.json
pip-audit --require=requirements.txt --desc
# Programmatic usage
from pip_audit._cli import audit
# Or parse JSON output
import subprocess, json
result = subprocess.run(["pip-audit", "--format=json"], capture_output=True, text=True)
vulns = json.loads(result.stdout)

Hash Verification

import hashlib
sha = hashlib.sha256()
with open("binary.exe", "rb") as f:
    for chunk in iter(lambda: f.read(8192), b""):
        sha.update(chunk)
print(sha.hexdigest())

Dependency Confusion Checks

Registry Check Command Risk
npm npm view <pkg> name Package exists publicly
PyPI pip index versions <pkg> Package exists publicly
Maven mvn dependency:resolve Artifact on Maven Central

Splunk SPL - Build Anomaly Detection

index=cicd sourcetype=build_logs
| where match(_raw, "(?i)(curl.*\|.*sh|wget.*chmod|--registry\s+http)")
| table _time build_id job_name _raw

Supply Chain Indicators

Indicator Severity Category
Known compromised package CRITICAL Package takeover
Non-standard registry URL HIGH Dependency confusion
Install scripts in deps MEDIUM Post-install hooks
Git URL dependencies MEDIUM Unpinned source
Pipe to shell in CI CRITICAL Remote code execution

References

standards.md1.6 KB

Standards and References - Hunting For Supply Chain Compromise

MITRE ATT&CK Mappings

Technique Name Description
T1195.001 Compromise Software Dependencies See attack.mitre.org/techniques/T1195/001
T1195.002 Compromise Software Supply Chain See attack.mitre.org/techniques/T1195/002
T1199 Trusted Relationship See attack.mitre.org/techniques/T1199

Detection Data Sources

Source Event ID Purpose
Sysmon 1 Process creation with command line
Sysmon 3 Network connection initiated
Sysmon 7 Image loaded (DLL)
Sysmon 10 Process access (LSASS)
Sysmon 11 File creation
Sysmon 12/13 Registry create/set
Sysmon 22 DNS query
Sysmon 25 Process tampering
Windows Security 4624 Successful logon
Windows Security 4625 Failed logon
Windows Security 4648 Explicit credential logon
Windows Security 4672 Special privileges assigned
Windows Security 4688 Process creation
Windows Security 4697 Service installed
Windows Security 4698 Scheduled task created
Windows Security 4769 Kerberos TGS requested
Windows Security 5140 Network share accessed

References

workflows.md3.0 KB

Detailed Hunting Workflow - Hunting For Supply Chain Compromise

Phase 1: Data Collection and Querying

Splunk SPL Query

index=sysmon EventCode=1
| where match(ParentImage, "(?i)(update|installer|setup|patch|deploy)")
| where match(Image, "(?i)(cmd|powershell|wscript|cscript|mshta)")
| where NOT match(ParentImage, "(?i)(Windows\\SoftwareDistribution)")
| table _time Computer User ParentImage Image CommandLine

KQL Query (Microsoft Defender for Endpoint)

DeviceProcessEvents
| where InitiatingProcessFileName matches regex @"(?i)(update|installer|setup|patch)"
| where FileName in~ ("cmd.exe","powershell.exe","wscript.exe")
| project Timestamp, DeviceName, InitiatingProcessFileName, FileName, ProcessCommandLine

Phase 2: Baseline and Anomaly Detection

Step 2.1 - Establish Normal Behavior Baseline

  • Collect 30 days of historical data for the targeted technique
  • Document expected patterns, frequencies, and legitimate use cases
  • Identify known false positive sources and document exceptions
  • Build statistical baseline (mean, standard deviation) for key metrics

Step 2.2 - Identify Anomalies

  • Compare current activity against the 30-day baseline
  • Flag events exceeding 3 standard deviations from normal
  • Prioritize anomalies by risk score and potential business impact
  • Cross-reference with threat intelligence for known IOCs

Phase 3: Investigation and Correlation

Step 3.1 - Deep Dive Analysis

  • For each anomaly, collect full process tree context
  • Correlate with network activity, file operations, and authentication events
  • Check binary signatures, file hashes, and certificate validity
  • Review user account context and access patterns

Step 3.2 - Attack Chain Reconstruction

  • Map findings to MITRE ATT&CK kill chain stages
  • Identify initial access vector if applicable
  • Trace lateral movement and privilege escalation paths
  • Determine data access and potential exfiltration

Phase 4: Validation and Response

Step 4.1 - True/False Positive Determination

  • Verify findings with system owners and IT operations
  • Check change management records for authorized activities
  • Validate user context (authorized actions vs. compromised account)
  • Document determination rationale for each finding

Step 4.2 - Response Actions

  • For confirmed threats: initiate incident response procedures
  • For detection gaps: create or update detection rules
  • For false positives: tune existing rules and update exclusions
  • Update threat hunting playbook with lessons learned

Phase 5: Documentation and Reporting

Step 5.1 - Hunt Report

  • Summarize hypothesis, methodology, and findings
  • Include all queries executed and their results
  • Document IOCs discovered and detection rules created
  • Provide recommendations for security improvements

Step 5.2 - Knowledge Base Update

  • Add findings to threat intelligence platform
  • Update MITRE ATT&CK coverage heatmap
  • Share detection rules via Sigma format
  • Schedule follow-up hunts for related techniques

Scripts 2

agent.py6.9 KB
Display-only source. This catalog never executes bundled scripts.
#!/usr/bin/env python3
"""Agent for hunting supply chain compromise indicators in software dependencies and builds."""

import json
import argparse
import hashlib
import re
import subprocess
from datetime import datetime
from pathlib import Path


KNOWN_COMPROMISED_PACKAGES = {
    "event-stream": "npm", "ua-parser-js": "npm", "coa": "npm",
    "colors": "npm", "faker": "npm", "node-ipc": "npm",
    "ctx": "pypi", "phpass": "pypi",
}


def scan_npm_lockfile(lockfile_path):
    """Scan package-lock.json for suspicious dependencies."""
    with open(lockfile_path) as f:
        data = json.load(f)
    findings = []
    packages = data.get("packages", data.get("dependencies", {}))
    for name, info in packages.items():
        pkg_name = name.split("node_modules/")[-1] if "node_modules/" in name else name
        if not pkg_name:
            continue
        if pkg_name in KNOWN_COMPROMISED_PACKAGES:
            findings.append({
                "package": pkg_name, "version": info.get("version", ""),
                "severity": "CRITICAL", "reason": "known_compromised",
            })
        resolved = info.get("resolved", "")
        if resolved and not resolved.startswith("https://registry.npmjs.org/"):
            findings.append({
                "package": pkg_name, "resolved": resolved,
                "severity": "HIGH", "reason": "non_standard_registry",
            })
        if info.get("hasInstallScript", False):
            findings.append({
                "package": pkg_name, "version": info.get("version", ""),
                "severity": "MEDIUM", "reason": "install_script",
            })
    return findings


def scan_pip_requirements(req_path):
    """Scan pip requirements.txt for suspicious packages."""
    findings = []
    with open(req_path) as f:
        for line in f:
            line = line.strip()
            if not line or line.startswith("#"):
                continue
            match = re.match(r"^([a-zA-Z0-9_.-]+)", line)
            if not match:
                continue
            pkg = match.group(1)
            if pkg.lower() in KNOWN_COMPROMISED_PACKAGES:
                findings.append({
                    "package": pkg, "line": line,
                    "severity": "CRITICAL", "reason": "known_compromised",
                })
            if "--index-url" in line or "--extra-index-url" in line:
                findings.append({
                    "package": pkg, "line": line,
                    "severity": "HIGH", "reason": "custom_index",
                })
            if re.search(r"git\+https?://", line):
                findings.append({
                    "package": pkg, "line": line,
                    "severity": "MEDIUM", "reason": "git_dependency",
                })
    return findings


def verify_binary_hashes(manifest_path):
    """Verify binary hashes against a known-good manifest."""
    with open(manifest_path) as f:
        manifest = json.load(f)
    results = []
    for entry in manifest:
        filepath = entry.get("path", "")
        expected_hash = entry.get("sha256", "")
        if not Path(filepath).exists():
            results.append({"path": filepath, "status": "MISSING", "severity": "HIGH"})
            continue
        sha = hashlib.sha256()
        with open(filepath, "rb") as bf:
            for chunk in iter(lambda: bf.read(8192), b""):
                sha.update(chunk)
        actual = sha.hexdigest()
        if actual != expected_hash:
            results.append({
                "path": filepath, "expected": expected_hash, "actual": actual,
                "status": "MISMATCH", "severity": "CRITICAL",
            })
    return results


def scan_build_logs(log_path):
    """Scan CI/CD build logs for supply chain indicators."""
    suspicious_patterns = [
        (r"curl\s+.*\|\s*(sh|bash)", "CRITICAL", "pipe_to_shell"),
        (r"wget\s+.*&&\s*chmod\s+\+x", "HIGH", "download_and_execute"),
        (r"npm\s+install\s+--registry\s+(?!https://registry\.npmjs\.org)", "HIGH", "custom_registry"),
        (r"pip\s+install\s+--index-url\s+(?!https://pypi\.org)", "HIGH", "custom_pypi"),
        (r"docker\s+pull\s+(?!docker\.io/|gcr\.io/|ghcr\.io/)", "MEDIUM", "untrusted_registry"),
    ]
    findings = []
    with open(log_path) as f:
        for i, line in enumerate(f, 1):
            for pattern, severity, category in suspicious_patterns:
                if re.search(pattern, line, re.IGNORECASE):
                    findings.append({
                        "line_number": i, "content": line.strip()[:300],
                        "pattern": category, "severity": severity,
                    })
    return findings


def check_dependency_confusion(internal_packages, public_registry="npm"):
    """Check if internal package names exist on public registries."""
    findings = []
    for pkg in internal_packages:
        try:
            if public_registry == "npm":
                result = subprocess.run(
                    ["npm", "view", pkg, "name"], capture_output=True, text=True, timeout=10)
            else:
                result = subprocess.run(
                    ["pip", "index", "versions", pkg], capture_output=True, text=True, timeout=10)
            if result.returncode == 0:
                findings.append({
                    "package": pkg, "registry": public_registry,
                    "severity": "CRITICAL", "reason": "dependency_confusion_risk",
                })
        except (subprocess.TimeoutExpired, FileNotFoundError):
            continue
    return findings


def main():
    parser = argparse.ArgumentParser(description="Supply Chain Compromise Hunter")
    parser.add_argument("--npm-lock", help="Path to package-lock.json")
    parser.add_argument("--pip-req", help="Path to requirements.txt")
    parser.add_argument("--manifest", help="Path to hash manifest JSON")
    parser.add_argument("--build-log", help="Path to CI/CD build log")
    parser.add_argument("--output", default="supply_chain_hunt_report.json")
    args = parser.parse_args()

    report = {"generated_at": datetime.utcnow().isoformat(), "findings": {}}

    if args.npm_lock:
        f = scan_npm_lockfile(args.npm_lock)
        report["findings"]["npm_scan"] = f
        print(f"[+] NPM lock findings: {len(f)}")

    if args.pip_req:
        f = scan_pip_requirements(args.pip_req)
        report["findings"]["pip_scan"] = f
        print(f"[+] Pip requirements findings: {len(f)}")

    if args.manifest:
        f = verify_binary_hashes(args.manifest)
        report["findings"]["hash_verification"] = f
        print(f"[+] Binary hash mismatches: {len([x for x in f if x.get('status') == 'MISMATCH'])}")

    if args.build_log:
        f = scan_build_logs(args.build_log)
        report["findings"]["build_log_scan"] = f
        print(f"[+] Build log findings: {len(f)}")

    with open(args.output, "w") as fout:
        json.dump(report, fout, indent=2, default=str)
    print(f"[+] Report saved to {args.output}")


if __name__ == "__main__":
    main()
process.py3.6 KB
Display-only source. This catalog never executes bundled scripts.
#!/usr/bin/env python3
"""Supply Chain Compromise Detection - Analyzes logs for T1195.002 indicators."""

import json, csv, argparse, datetime, re
from collections import defaultdict
from pathlib import Path

DETECTION_PATTERNS = [
    r'update.*cmd\\.exe',
    r'installer.*powershell',
    r'setup.*wscript',
    r'patch.*mshta',
]

def parse_logs(path):
    p = Path(path)
    if p.suffix == ".json":
        with open(p, encoding="utf-8") as f:
            data = json.load(f)
            return data if isinstance(data, list) else data.get("events", [])
    elif p.suffix == ".csv":
        with open(p, encoding="utf-8-sig") as f:
            return [dict(r) for r in csv.DictReader(f)]
    return []

def analyze_event(event):
    cmd = event.get("CommandLine", event.get("command_line", event.get("ProcessCommandLine", "")))
    content = event.get("Task_Content", event.get("Parameters", event.get("RawEventData", "")))
    search_text = f"{cmd} {content}"
    risk = 0
    indicators = []
    for pattern in DETECTION_PATTERNS:
        if re.search(pattern, search_text, re.IGNORECASE):
            risk += 25
            indicators.append(f"Pattern match: {pattern}")
    if not indicators:
        return None
    risk = min(risk, 100)
    return {
        "technique": "T1195.002",
        "command_line": cmd[:500] if cmd else content[:500],
        "hostname": event.get("Computer", event.get("DeviceName", event.get("hostname", "unknown"))),
        "user": event.get("User", event.get("AccountName", event.get("UserId", "unknown"))),
        "timestamp": event.get("_time", event.get("timestamp", event.get("UtcTime", event.get("Timestamp", "")))),
        "risk_score": risk,
        "risk_level": "CRITICAL" if risk >= 75 else "HIGH" if risk >= 50 else "MEDIUM" if risk >= 25 else "LOW",
        "indicators": indicators,
    }

def run_hunt(input_path, output_dir):
    print(f"[*] Supply Chain Compromise Hunt - {datetime.datetime.now().isoformat()}")
    events = parse_logs(input_path)
    findings = [f for f in (analyze_event(e) for e in events) if f]
    Path(output_dir).mkdir(parents=True, exist_ok=True)
    slug = "hunting_for_supply_c"
    with open(Path(output_dir) / f"{slug}_findings.json", "w", encoding="utf-8") as f:
        json.dump({"hunt_id": f"TH-{datetime.date.today()}", "total_events": len(events), "findings": findings}, f, indent=2)
    with open(Path(output_dir) / "hunt_report.md", "w", encoding="utf-8") as f:
        f.write(f"# Supply Chain Compromise Hunt Report\n\n")
        f.write(f"**Date**: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
        f.write(f"**Findings**: {len(findings)}\n\n")
        for finding in sorted(findings, key=lambda x: x["risk_score"], reverse=True)[:20]:
            f.write(f"### [{finding['risk_level']}] {finding['technique']}\n")
            f.write(f"- **Host**: {finding['hostname']}\n")
            f.write(f"- **Indicators**: {', '.join(finding['indicators'])}\n\n")
    print(f"[+] {len(findings)} findings written to {output_dir}")

def main():
    p = argparse.ArgumentParser(description="Supply Chain Compromise Detection")
    sp = p.add_subparsers(dest="cmd")
    h = sp.add_parser("hunt"); h.add_argument("--input", "-i", required=True); h.add_argument("--output", "-o", default="./hunting_for_sup_output")
    sp.add_parser("queries")
    args = p.parse_args()
    if args.cmd == "hunt": run_hunt(args.input, args.output)
    elif args.cmd == "queries":
        print("=== Detection Queries ===")
        print("See references/workflows.md for platform-specific queries")
    else: p.print_help()

if __name__ == "__main__": main()

Assets 1

template.mdtext/markdown · 2.6 KB
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