ayoubfaouzi/al-khaser

Public malware techniques used in the wild: Virtual Machine, Emulation, Debuggers, Sandbox detection.

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Summary Information

Updated 32 minutes ago
Added to GitGenius on September 9th, 2026
Created on November 12th, 2015
Open Issues & Pull Requests: 42 (+0)
GitHub issues: Enabled
Number of forks: 1,253
Total Stargazers: 7,119 (+0)
Total Subscribers: 235 (+0)

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Detailed Description

Al-Khaser is a proof-of-concept malware application designed to test the effectiveness of anti-malware and anti-analysis systems by demonstrating common evasion techniques.

The tool addresses the need to validate security defenses by implementing a wide range of malware evasion tactics observed in real-world threats. It performs anti-debugging checks through multiple methods including debugger detection APIs, process environment block inspection, heap flag analysis, and hardware and software breakpoint detection. The application also implements anti-injection techniques by enumerating loaded modules through various Windows APIs and walking memory structures to identify hidden modules. Additionally, it includes anti-dumping measures, anti-VM detection, anti-disassembly tricks, and timing-based sandbox evasion through methods like RDTSC instructions, sleep acceleration detection, and multimedia timer checks.

Organizations developing anti-malware solutions, sandbox environments, or malware analysis platforms should consider this tool for validating their defenses. Security teams responsible for endpoint protection can use it to stress-test their anti-debug implementations and ensure their analysis environments remain sufficiently hidden from evasion detection. The tool is particularly suited for red team exercises and defensive security research where understanding real-world evasion techniques is critical. Developers can download prebuilt binaries for both x86 and x64 architectures from the releases page.

The project maintains a comprehensive feature set covering established anti-analysis techniques documented in real malware samples. Development activity shows ongoing refinement of evasion methods, with the codebase organized around distinct categories of anti-analysis tactics. The project explicitly invites contributions of newly observed anti-analysis tricks encountered in malware, indicating an approach to keeping the technique collection aligned with emerging threats in the wild.