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mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
70 lines
2.6 KiB
Markdown
70 lines
2.6 KiB
Markdown
---
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name: analyzing-malware-sandbox-evasion-techniques
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description: Detect sandbox evasion techniques in malware samples by analyzing timing
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checks, VM artifact queries, user interaction detection, and sleep inflation patterns
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from Cuckoo/AnyRun behavioral reports
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domain: cybersecurity
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subdomain: malware-analysis
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tags:
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- sandbox-evasion
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- malware-analysis
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- cuckoo
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- anyrun
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- mitre-attack
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- virtualization-detection
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- behavioral-analysis
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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d3fend_techniques:
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- Platform Hardening
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- Restore Object
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- Process Analysis
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- System Call Filtering
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- Restore Software
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nist_csf:
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- DE.AE-02
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- RS.AN-03
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- ID.RA-01
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- DE.CM-01
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mitre_attack:
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- T1497.001
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- T1497.003
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- T1480
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- T1027.002
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---
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# Analyzing Malware Sandbox Evasion Techniques
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## Overview
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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.
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## When to Use
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- When investigating security incidents that require analyzing malware sandbox evasion techniques
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Cuckoo Sandbox 2.0+ or AnyRun account for behavioral analysis reports
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- Python 3.8+ with json library for report parsing
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- Behavioral report exports in JSON format
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## Steps
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1. Parse Cuckoo/AnyRun behavioral report JSON files
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2. Extract API call sequences for timing-related functions
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3. Identify VM artifact detection via registry queries and WMI calls
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4. Detect sleep inflation by comparing requested vs actual sleep durations
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5. Flag user interaction checks (GetCursorPos, GetAsyncKeyState patterns)
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6. Score evasion sophistication based on technique count and diversity
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7. Map detected techniques to MITRE ATT&CK T1497 sub-techniques
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## Expected Output
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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).
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