/analyzing-malware-behavior-with-cuckoo-sandbox
Detonate malware samples in Cuckoo Sandbox to observe runtime behavior
$ npx -y skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-behavior-with-cuckoo-sandbox --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/analyzing-malware-behavior-with-cuckoo-sandbox
Context preview
The summary Claude sees to decide when to auto-load this skill.
Detonate malware samples in Cuckoo Sandbox to observe runtime behavior
SKILL.md
analyzing-malware-behavior-with-cuckoo-sandbox.SKILL.mdname: analyzing-malware-behavior-with-cuckoo-sandbox
description: 'Detonate malware samples in Cuckoo Sandbox to observe runtime behavior
— process creation, file system and registry changes, network communications,
and API calls — and generate behavioral reports for classification and IOC extraction.
Use when a sample has passed static triage and needs dynamic/behavioral analysis,
when mapping a full infection chain, or when building YARA/behavioral signatures
from observed sandbox activity.
'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware
- dynamic-analysis
- sandbox
- Cuckoo
- behavioral-analysis
version: 1.0.0
author: mahipal
license: Apache-2.0
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
mitre_attack:
- T1497
- T1055
- T1071
- T1027
Analyzing Malware Behavior with Cuckoo Sandbox
When to Use
- A suspicious sample passed static analysis triage and requires behavioral observation in a controlled environment
- You need to capture network traffic, file drops, registry modifications, and API calls from a malware execution
- Determining the full infection chain including second-stage payload downloads and persistence mechanisms
- Generating behavioral signatures and YARA rules based on observed runtime activity
- Automated analysis of bulk malware samples requiring consistent reporting
**Do not use** when the sample is a known ransomware variant that may spread via network shares in a misconfigured sandbox; verify network isolation first.
Prerequisites
- Cuckoo Sandbox 3.x installed on a dedicated analysis server (Ubuntu 22.04 recommended)
- Guest VMs configured with Windows 10/11 snapshots (Cuckoo agent installed, snapshots taken at clean state)
- VirtualBox, KVM, or VMware configured as the Cuckoo virtualization backend
- Isolated network with InetSim or FakeNet-NG for simulating internet services
- Suricata or Snort integrated for network-level signature matching during analysis
- Sufficient disk space for PCAP captures and memory dumps (minimum 500 GB recommended)
Workflow
Step 1: Submit Sample to Cuckoo
Submit the malware sample for automated analysis:
# Submit via command line
cuckoo submit /path/to/suspect.exe
# Submit with specific analysis timeout (300 seconds)
cuckoo submit --timeout 300 /path/to/suspect.exe
# Submit with specific VM and analysis package
cuckoo submit --machine win10_x64 --package exe --timeout 300 /path/to/suspect.exe
# Submit via REST API
curl -F "file=@suspect.exe" -F "timeout=300" -F "machine=win10_x64" \
http://localhost:8090/tasks/create/file
# Submit URL for analysis
curl -F "url=http://malicious-site.com/payload" -F "timeout=300" \
http://localhost:8090/tasks/create/url
# Check task status
curl http://localhost:8090/tasks/view/1 | jq '.task.status'
Step 2: Monitor Execution in Real-Time
Track the analysis progress and observe live behavior:
# Watch Cuckoo analysis log
tail -f /opt/cuckoo/log/cuckoo.log
# Monitor analysis task status
cuckoo status
# Access Cuckoo web interface for live screenshots and process tree
# Navigate to http://localhost:8080/analysis/<task_id>/
Key behavioral events to watch during execution:
- Process creation chain (parent-child relationships)
- Network connection attempts to external IPs
- File drops in temporary directories or system folders
- Registry modifications to Run keys or service entries
- API calls related to encryption (CryptEncrypt), injection (WriteProcessMemory), or evasion
Step 3: Analyze Process Activity
Review the process tree and API call trace from the Cuckoo report:
# Parse Cuckoo JSON report programmatically
import json
with open("/opt/cuckoo/storage/analyses/1/reports/report.json") as f:
report = json.load(f)
# Process tree analysis
for process in report["behavior"]["processes"]:
pid = process["pid"]
ppid = process["ppid"]
name = process["process_name"]
print(f"PID: {pid} PPID: {ppid} Name: {name}")
# Extract suspicious API calls
for call in process["calls"]:
api = call["api"]
if api in ["CreateRemoteThread", "VirtualAllocEx", "WriteProcessMemory",
"NtCreateThreadEx", "RegSetValueExA", "URLDownloadToFileA"]:
args = {arg["name"]: arg["value"] for arg in call["arguments"]}
print(f" [!] {api}({args})")Step 4: Review Network Activity
Examine network connections, DNS queries, and HTTP requests:
# Network analysis from Cuckoo report
network = report["network"]
# DNS resolutions
print("DNS Queries:")
for dns in network.get("dns", []):
print(f" {dns['request']} -> {dns.get('answers', [])}")
# HTTP requests
print("\nHTTP Requests:")
for http in network.get("http", []):
print(f" {http['method']} {http['uri']} (Host: {http['host']})")
if http.get("body"):
print(f" Body: {http['body'][:200]}")
# TCP connections
print("\nTCP Connections:")
for tcp in network.get("tcp", []):
print(f" {tcp['src']}:{tcp['sport']} -> {tcp['dst']}:{tcp['dport']}")
# Extract PCAP for deeper Wireshark analysis
# PCAP location: /opt/cuckoo/storage/analyses/1/dump.pcapStep 5: Examine File System and Registry Changes
Document persistence mechanisms and dropped files:
# File operations
print("Files Created/Modified:")
for f in report["behavior"].get("summary", {}).get("files", []):
print(f" {f}")
# Dropped files with hashes
print("\nDropped Files:")
for dropped in report.get("dropped", []):
print(f" Path: {dropped['filepath']}")
print(f" SHA-256: {dropped['sha256']}")
print(f" Size: {dropped['size']} bytes")
print(f" Type: {dropped['type']}")
# Registry modifications
print("\nRegistry Keys Modified:")
for key in report["behavior"].get("summary", {}).get("keys", []):
print(f" {key}")Step 6: Review Signatures and Scoring
Check Cuckoo's behavioral signatures and threat scoring:
# Behavioral signat
Read more
name: analyzing-malware-behavior-with-cuckoo-sandbox description: 'Detonate malware samples in Cuckoo Sandbox to observe runtime behavior — process creation, file system and registry changes, network communications, and API calls — and generate behavioral reports for classification and IOC extraction. Use when a sample has passed static triage and needs dynamic/behavioral analysis, when mapping a full infection chain, or when building YARA/behavioral signatures from observed sandbox activity. ' domain: cybersecurity subdomain: malware-analysis tags: - malware - dynamic-analysis - sandbox - Cuckoo - behavioral-analysis version: 1.0.0 author: mahipal license: Apache-2.0 nist_csf: - DE.AE-02 - RS.AN-03 - ID.RA-01 - DE.CM-01 mitre_attack: - T1497 - T1055 - T1071 - T1027
Analyzing Malware Behavior with Cuckoo Sandbox
When to Use
- A suspicious sample passed static analysis triage and requires behavioral observation in a controlled environment
- You need to capture network traffic, file drops, registry modifications, and API calls from a malware execution
- Determining the full infection chain including second-stage payload downloads and persistence mechanisms
- Generating behavioral signatures and YARA rules based on observed runtime activity
- Automated analysis of bulk malware samples requiring consistent reporting
**Do not use** when the sample is a known ransomware variant that may spread via network shares in a misconfigured sandbox; verify network isolation first.
Prerequisites
- Cuckoo Sandbox 3.x installed on a dedicated analysis server (Ubuntu 22.04 recommended)
- Guest VMs configured with Windows 10/11 snapshots (Cuckoo agent installed, snapshots taken at clean state)
- VirtualBox, KVM, or VMware configured as the Cuckoo virtualization backend
- Isolated network with InetSim or FakeNet-NG for simulating internet services
- Suricata or Snort integrated for network-level signature matching during analysis
- Sufficient disk space for PCAP captures and memory dumps (minimum 500 GB recommended)
Workflow
Step 1: Submit Sample to Cuckoo
Submit the malware sample for automated analysis:
# Submit via command line cuckoo submit /path/to/suspect.exe # Submit with specific analysis timeout (300 seconds) cuckoo submit --timeout 300 /path/to/suspect.exe # Submit with specific VM and analysis package cuckoo submit --machine win10_x64 --package exe --timeout 300 /path/to/suspect.exe # Submit via REST API curl -F "file=@suspect.exe" -F "timeout=300" -F "machine=win10_x64" \ http://localhost:8090/tasks/create/file # Submit URL for analysis curl -F "url=http://malicious-site.com/payload" -F "timeout=300" \ http://localhost:8090/tasks/create/url # Check task status curl http://localhost:8090/tasks/view/1 | jq '.task.status'
Step 2: Monitor Execution in Real-Time
Track the analysis progress and observe live behavior:
# Watch Cuckoo analysis log tail -f /opt/cuckoo/log/cuckoo.log # Monitor analysis task status cuckoo status # Access Cuckoo web interface for live screenshots and process tree # Navigate to http://localhost:8080/analysis/<task_id>/
Key behavioral events to watch during execution:
- Process creation chain (parent-child relationships)
- Network connection attempts to external IPs
- File drops in temporary directories or system folders
- Registry modifications to Run keys or service entries
- API calls related to encryption (CryptEncrypt), injection (WriteProcessMemory), or evasion
Step 3: Analyze Process Activity
Review the process tree and API call trace from the Cuckoo report:
# Parse Cuckoo JSON report programmatically
import json
with open("/opt/cuckoo/storage/analyses/1/reports/report.json") as f:
report = json.load(f)
# Process tree analysis
for process in report["behavior"]["processes"]:
pid = process["pid"]
ppid = process["ppid"]
name = process["process_name"]
print(f"PID: {pid} PPID: {ppid} Name: {name}")
# Extract suspicious API calls
for call in process["calls"]:
api = call["api"]
if api in ["CreateRemoteThread", "VirtualAllocEx", "WriteProcessMemory",
"NtCreateThreadEx", "RegSetValueExA", "URLDownloadToFileA"]:
args = {arg["name"]: arg["value"] for arg in call["arguments"]}
print(f" [!] {api}({args})")Step 4: Review Network Activity
Examine network connections, DNS queries, and HTTP requests:
# Network analysis from Cuckoo report
network = report["network"]
# DNS resolutions
print("DNS Queries:")
for dns in network.get("dns", []):
print(f" {dns['request']} -> {dns.get('answers', [])}")
# HTTP requests
print("\nHTTP Requests:")
for http in network.get("http", []):
print(f" {http['method']} {http['uri']} (Host: {http['host']})")
if http.get("body"):
print(f" Body: {http['body'][:200]}")
# TCP connections
print("\nTCP Connections:")
for tcp in network.get("tcp", []):
print(f" {tcp['src']}:{tcp['sport']} -> {tcp['dst']}:{tcp['dport']}")
# Extract PCAP for deeper Wireshark analysis
# PCAP location: /opt/cuckoo/storage/analyses/1/dump.pcapStep 5: Examine File System and Registry Changes
Document persistence mechanisms and dropped files:
# File operations
print("Files Created/Modified:")
for f in report["behavior"].get("summary", {}).get("files", []):
print(f" {f}")
# Dropped files with hashes
print("\nDropped Files:")
for dropped in report.get("dropped", []):
print(f" Path: {dropped['filepath']}")
print(f" SHA-256: {dropped['sha256']}")
print(f" Size: {dropped['size']} bytes")
print(f" Type: {dropped['type']}")
# Registry modifications
print("\nRegistry Keys Modified:")
for key in report["behavior"].get("summary", {}).get("keys", []):
print(f" {key}")Step 6: Review Signatures and Scoring
Check Cuckoo's behavioral signatures and threat scoring:
# Behavioral signat
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
Repo: mukul975/Anthropic-Cybersecurity-Skills
Other skills on cybersecurity-skills.
- /abusing-dpapi-for-credential-access
Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket's dpapi.py, including domain-wide decryption via the DPAPI backup key. Use
Open skill - /abusing-shadow-credentials-for-privesc
Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target's NT hash without a password reset. Use when BloodHound shows
Open skill - /achieving-cmmc-level-2-compliance
Prepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the
Open skill - /acquiring-disk-image-with-dd-and-dcfldd
Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving
Open skill - /analyzing-active-directory-acl-abuse
Detect dangerous ACL misconfigurations in Active Directory using ldap3
Open skill - /analyzing-android-malware-with-apktool
Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and
Open skill

