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analyzing-malware-sandbox-evasion-techniques

Review Cuckoo or AnyRun behavior reports for signs that malware is avoiding an analysis environment. Identify timing checks, virtual machine detection, and user activity checks, then produce a JSON report with evidence, technique mappings, and an evasion sophistication score.

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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.

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适用场景

  • Investigating malware that stays inactive in a sandbox
  • Reviewing behavior reports for analysis evasion
  • Developing detections for anti-analysis behavior
  • Documenting evasion evidence for incident investigations

包含的文件(4)

  • SKILL.md2.8 KB
  • LICENSE11.0 KB
  • references/api-reference.md2.0 KB
  • scripts/agent.py7.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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).

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