analyzing-malware-sandbox-evasion-techniques

name	analyzing-malware-sandbox-evasion-techniques
description	Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports. Use when detecting sandbox evasion techniques in malware samples by analyzing timing.
domain	cybersecurity
subdomain	malware-analysis
tags	["sandbox-evasion","malware-analysis","cuckoo","anyrun","mitre-attack","virtualization-detection","behavioral-analysis"]
version	1.0
author	oyi77
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"]

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

Trigger phrases:

• "analyzing malware sandbox evasion techniques"

• "Detect sandbox evasion techniques in malware samples by analyzing timing checks,"

• 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

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}

1. Parse Cuckoo/AnyRun behavioral report JSON files
2. Extract API call sequences for timing-related functions