Analyzing Malware Sandbox Evasion Techniques

---
name: analyzing-malware-sandbox-evasion-techniques
description: Detect sandbox and VM evasion techniques in malware samples by analyzing
  timing checks, VM/hypervisor artifact queries, user-interaction checks, and sleep-inflation
  patterns from Cuckoo or AnyRun behavioral reports. Use when a sample shows no or
  minimal activity in a sandbox, when a behavioral report needs review for evasion
  indicators, or when building detections for anti-analysis techniques.
domain: cybersecurity
subdomain: malware-analysis
tags:
- sandbox-evasion
- malware-analysis
- cuckoo
- anyrun
- mitre-attack
- virtualization-detection
- behavioral-analysis
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- Platform Hardening
- Restore Object
- Process Analysis
- System Call Filtering
- Restore Software
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
mitre_attack:
- T1497.001
- T1497.003
- T1480
- T1027.002
---

# Analyzing Malware Sandbox Evasion Techniques

## Overview

Sandbox evasion (MITRE ATT&CK T1497) allows malware to detect analysis environments and alter behavior to avoid detection. This skill analyzes behavioral reports from Cuckoo Sandbox and AnyRun for evasion indicators including timing-based checks (GetTickCount, QueryPerformanceCounter, sleep inflation), VM artifact detection (registry keys, MAC address prefixes, process names like vmtoolsd.exe), user interaction checks (mouse movement, keyboard input), and environment fingerprinting (disk size, CPU count, RAM). Detection rules flag samples exhibiting these behaviors for deeper manual analysis.


## When to Use

- When investigating security incidents that require analyzing malware sandbox evasion techniques
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques

## Prerequisites

- Cuckoo Sandbox 2.0+ or AnyRun account for behavioral analysis reports
- Python 3.8+ with json library for report parsing
- Behavioral report exports in JSON format

## Steps

1. Parse Cuckoo/AnyRun behavioral report JSON files
2. Extract API call sequences for timing-related functions
3. Identify VM artifact detection via registry queries and WMI calls
4. Detect sleep inflation by comparing requested vs actual sleep durations
5. Flag user interaction checks (GetCursorPos, GetAsyncKeyState patterns)
6. Score evasion sophistication based on technique count and diversity
7. Map detected techniques to MITRE ATT&CK T1497 sub-techniques

## Expected Output

JSON report listing detected evasion techniques with MITRE ATT&CK mapping, API call evidence, evasion sophistication score, and classification of evasion categories (timing, VM detection, user interaction, environment fingerprinting).