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sica-fondt/knowledge/cyber-skills/skills/analyzing-malware-sandbox-evasion-techniques/SKILL.md
Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure 
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

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name description domain subdomain tags version author license d3fend_techniques nist_csf
analyzing-malware-sandbox-evasion-techniques 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 cybersecurity malware-analysis
sandbox-evasion
malware-analysis
cuckoo
anyrun
mitre-attack
virtualization-detection
behavioral-analysis
1.0 mahipal Apache-2.0
Platform Hardening
Restore Object
Process Analysis
System Call Filtering
Restore Software
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

  • 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).