Atlas / Skills / mukul975 / Analyzing Malware Behavior With Cuckoo Sandbox

Analyzing Malware Behavior With Cuckoo SandboxSAFE

skills/mukul975/analyzing-malware-behavior-with-cuckoo-sandbox

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 ·

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
1.0.0
Hosts
—
License
Apache-2.0
Stars
33,870
01

Overview

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 ·

Read from source at commit 6c59587be632OBSERVED · 2026-10-07
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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:

```bash
# 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 "[email protected]" -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:

```bash
# 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:

```python
# 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:

```python
# 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.pcap
```

### Step 5: Examine File System and Registry Changes

Document persistence mechanisms and dropped files:

```python
# 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:

```python
# Behavioral signat
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codePASS
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
declared (3 observation(s))
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-07 · audit v0.4.1 · source sha 6c59587be632full audit observations/trust-audit/skill/mukul975__analyzing-malware-behavior-with-cuckoo-sandbox.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-076c59587be632SAFEB89first audit
05

Questions

What does the Analyzing Malware Behavior With Cuckoo Sandbox skill do?

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 ·

Is Analyzing Malware Behavior With Cuckoo Sandbox safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Analyzing Malware Behavior With Cuckoo Sandbox access on my machine?

The audit observed that it reaches the network. Each of those is consistent with what it says it does. Secrets in the source: none found.

How current is this page?

The grade is for one exact copy of the source (6c59587be632), read on 2026-10-07. The repository is watched, and a new audit runs when it changes — this is the first audit.

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