analyzing-malware-sandbox-evasion-techniques

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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

AI & Automation 40 stars 10 forks Updated today MIT

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Skill Content

# 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, GetA...

Details

Author
26zl
Repository
26zl/cybersec-toolkit
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

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