analyst security skill risk: medium

Malware Sandbox Evasion Techniques Analyzer

Parses Cuckoo Sandbox or AnyRun behavioral JSON reports to detect timing checks, VM artifacts, user interaction patterns, and environment fingerprinting, then outputs a JSON report…

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---
name: analyzing-malware-sandbox-evasion-techniques
description: "Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction"
---
# 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).

REQUIRED CONTEXT

Cuckoo Sandbox or AnyRun behavioral report in JSON format

EXPECTED OUTPUT

Format

json

Schema

json_schema · detected evasion techniques, MITRE ATT&CK mapping, API call evidence, evasion sophistication score, classification of evasion categories

Constraints

list detected evasion techniques
include MITRE ATT&CK mapping
include API call evidence
include evasion sophistication score
classify into timing/VM detection/user interaction/environment fingerprinting

SUCCESS CRITERIA

Detect sandbox evasion techniques from behavioral reports
Map techniques to MITRE ATT&CK T1497
Include API call evidence and sophistication score

CAVEATS

Dependencies

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

Missing context

Exact schema or field names expected in the input JSON behavioral reports
Concrete thresholds or examples for classifying technique severity

Ambiguities

Scoring method for 'evasion sophistication' is described only at a high level ('based on technique count and diversity') without explicit criteria or formula.

QUALITY

OVERALL: 0.79
CLARITY: 0.88
SPECIFICITY: 0.78
REUSABILITY: 0.72
COMPLETENESS: 0.81

IMPROVEMENT SUGGESTIONS

Add an explicit scoring rubric or pseudocode for the sophistication score.
Include a minimal example of input JSON and corresponding output JSON.

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.