A Measurement Study of AI-Environment Realism Gaps in Malware-Analysis Sandboxes

2026-07-15

AI Summary

This work addresses the challenge posed by environment-aware malware that evades detection by identifying sandbox artifacts, a problem exacerbated by the inability of existing sandboxes to faithfully emulate AI execution environments. We propose AIprint, a novel probing framework that introduces AI-specific environmental artifacts—such as configuration files, cache states, and service footprints—as a new dimension for detecting sandbox evasion. Leveraging artifacts extracted from 450 open-source GitHub projects, we conduct comparative experiments across seven sandboxes and three real-world AI hosts, revealing 12 critical artifacts exclusive to genuine AI environments. Our findings demonstrate that AI tool installations leave discernible traces and that high-fidelity simulation incurs substantially higher costs than superficial deception. This study exposes the limitations of conventional virtual machine detection in AI contexts and establishes the first quantifiable framework for evaluating AI environment realism.

Abstract

Sandboxing remains a core technique for observing suspicious program behavior, yet environment-aware malware increasingly suppresses execution when analysis is suspected. Prior generations of sandbox evasion focused on virtualization artifacts, timing discrepancies, and wear-and-tear realism. In this paper, we present the first systematic measurement study of AI-environment artifacts as a new sandbox-evasion surface. We operationalize this realism gap through AIprint, a probe framework that captures persistent artifacts left behind by AI-capable software ecosystems, including AI-assistant configuration directories, model caches, environment variables, local inference services, and package dependencies. We systematically extract 450 unique artifacts from 284 open-source AI projects on GitHub, compile them into unprivileged Windows probes, and evaluate them across seven commercial and open-source sandbox backends together with three AI-capable reference hosts. Our results show that traditional VM-detection baselines fail to reliably distinguish real AI-capable systems from modern sandboxes, whereas twelve AI-environment artifacts appear on the reference hosts and on none of the evaluated backends. A controlled 214-step installation experiment establishes a causal relationship between AI tool and package installation and measurable AI-environment artifact accumulation, while adaptive spoofing experiments reveal a fundamental operational asymmetry: reproducing convincing AI software environments is substantially more expensive than detecting shallow spoofing.

Problem

• sandbox evasion
• AI-environment realism
• malware analysis
• environment artifacts
• realism gap

Innovation

• AI-environment realism
• sandbox evasion
• AIprint
• artifact measurement
• malware analysis