mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
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
64 lines
2.5 KiB
Markdown
64 lines
2.5 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 checks, VM artifact queries, user interaction
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detection, and sleep inflation patterns 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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---
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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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