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Analyzing Malware Sandbox Evasion Techniques

Free

Detect evasion techniques in malware behavioral reports.

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Free · Opens the source repo

What Analyzing Malware Sandbox Evasion Techniques does

This skill is designed for cybersecurity professionals who need to analyze malware samples that may be employing sandbox evasion techniques. By utilizing behavioral reports from Cuckoo Sandbox or AnyRun, the skill systematically identifies various evasion indicators such as timing checks, VM artifact queries, user interaction checks, and sleep inflation patterns. These indicators are crucial for understanding how malware can detect when it is being analyzed in a controlled environment and subsequently alter its behavior to evade detection.

The skill works by parsing JSON exports of behavioral reports, extracting relevant API call sequences, and flagging suspicious behaviors. For instance, it looks for timing-related functions like GetTickCount and QueryPerformanceCounter, which can indicate attempts to manipulate execution timing. It also checks for VM artifacts, such as specific registry keys or process names that suggest the malware is aware it is running in a virtualized environment. User interaction checks, which assess whether the malware is being interacted with, are also monitored to gauge the sophistication of the evasion techniques employed.

This tool is particularly useful for Security Operations Center (SOC) analysts and threat hunters who are tasked with investigating potential malware incidents. It provides a structured approach to identifying and documenting evasion techniques, which can then inform the development of detection rules and improve overall security monitoring coverage against these advanced threats. By mapping detected techniques to the MITRE ATT&CK framework, users can better understand the context and severity of the evasion tactics employed.

In summary, this skill serves as a valuable resource for cybersecurity professionals looking to enhance their malware analysis capabilities, specifically in detecting and understanding sandbox evasion techniques. It enables users to perform a thorough examination of malware behavior, leading to improved threat detection and incident response strategies.

When to use it

Use this skill when analyzing suspicious malware samples that may exhibit sandbox evasion behaviors, particularly during security incident investigations.

When not to use it

This skill is not suitable for analyzing benign software or when no behavioral reports are available for the malware in question.

What you can build with it

Investigating Malware Incidents

Use this skill to analyze suspicious malware samples during security investigations, identifying potential evasion tactics.

Building Detection Rules

Leverage the findings from this skill to create effective detection rules for monitoring sandbox evasion techniques.

Validating Security Coverage

Employ this skill to assess and validate your security monitoring capabilities against known evasion techniques.

How to install Analyzing Malware Sandbox Evasion Techniques

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1. Install with the skills CLI

npx skills add mukul975/anthropic-cybersecurity-skills/analyzing-malware-sandbox-evasion-techniques --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by mukul975

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).

Frequently asked questions about Analyzing Malware Sandbox Evasion Techniques

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