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

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TLDR

• What: AIprints is a sandbox evasion framework that detects the presence or absence of persistent local AI software environment artifacts (e.g., Claude Code/Cursor configs, HuggingFace model caches, local Ollama servers, API keys) to differentiate sterile analysis sandboxes from genuine AI-capable workstations.
• Who's at risk: Automated malware-analysis sandboxes (such as ANY.RUN, CAPEv2, Triage, Hybrid Analysis, and VirusTotal) and threat intelligence pipelines that fail to simulate modern developer/enterprise environments.
• Key number: Twelve "Class C" host-exclusive AI artifacts (including .cursor, OPENAI_API_KEY, and Ollama port 11434) appeared consistently on actual AI-capable hosts but were detected on 0% (none) of the evaluated sandbox backends, making them highly reliable evasion indicators.

As AI assistants like Cursor, Claude Code, and GitHub Copilot become ubiquitous developer and enterprise tools, malware is adapting to look for their footprint. Traditional sandbox evasion focused on hiding virtualization indicators, but a new breed of malware is shifting to "environment-aware" evasion by verifying if a host actually runs AI workloads. If malware executing on a system detects a sterile environment devoid of model caches, local inference ports, and API configurations, it can safely assume it is being analyzed inside a sandbox and suppress its malicious behavior.

Background / Problem Setup

Sandbox evasion has evolved through four distinct generations. Initially, defenders scrubbed obvious virtualization artifacts like hypervisor registry keys or MAC addresses. In the second generation, malware analyzed instruction latency and cache behavior, prompting defenders to deploy bare-metal analysis environments. The third generation introduced "wear-and-tear" checks—analyzing browser history, process counts, and user files to verify human activity (Miramirkhani et al. [8]).

Today, we are witnessing a transition to Fourth-Generation Evasion (AI-Environment Realism). Rather than asking what the system is, malware probes what the system runs.

Unlike prior generations, mitigating this gap is not a simple matter of scrubbing static files or modifying registry keys. It requires sandbox operators to deploy and continuously maintain complex, functional AI software ecosystems.

Evolution of Sandbox Evasion & Defense Costs

Methodology

To measure this gap, the authors developed AIprint, a framework designed to systematically extract, package, and evaluate AI-environment artifacts on target hosts.

Step 1: Artifact Extraction

The authors crawled GitHub to analyze configuration files from real-world AI ecosystem projects. They curated two distinct datasets:

1. Trending-41: 41 actively maintained projects from GitHub Trending (using AI keywords).
2. Topics-253: 253 Windows-compatible projects associated with high-popularity AI topics (such as ai, llm, and mcp).

By parsing READMEs, Python dependency manifests, and Dockerfiles, the pipeline extracted 450 unique artifacts across four taxonomic categories: 19 directories, 144 environment variables, 28 network ports, and 264 Python packages.

Step 2: Probe Compilation

These 450 artifacts were compiled into standalone, unprivileged C++ Windows executables (compiled with MSVC in 32-bit and 64-bit formats). Probes checked for:

• Directories: Invoked PathFileExistsW() on typical AI tool paths (e.g., %USERPROFILE%\.cursor).
• Environment Variables: Invoked GetEnvironmentVariableW() to check for variables like OPENAI_API_KEY (without inspecting contents).
• Network Ports: Attempted local TCP connections via socket() and connect().
• Python Packages: Verified the presence of packages within detected site-packages/ directories.

To bypass sandbox hash-caching, the probes used non-semantic PE modifications to ensure unique binary signatures across multiple submissions.

Step 3: Deployment & Verification

Probes were executed across seven major sandbox backends and compared against three physical, AI-capable reference hosts (running real enterprise/developer workloads on an Intel Core i9-13900KF / RTX 4090 GPU baseline). Additionally, to validate causality, the authors performed a controlled 214-step cumulative installation on a clean machine to track how the artifact footprint expands over time.

Key Results

The empirical results paint a grim picture for automated malware-analysis pipelines.

1. Traditional VM Baselines Have Failed

As Section 6.1 describes, traditional VM detection checks (using 269 SOTA indicators from tools like al-khaser [12] and pafish) are no longer effective at distinguishing sandboxes from real hosts. Modern physical developer machines with Hyper-V or WSL2 enabled by default actually trigger more VM indicators than modern hardened sandboxes do.

The reference hosts triggered an average of 9.1% of SOTA VM checks, whereas evaluated sandboxes triggered between 1.9% and 7.6% (Table 4).

2. AIprint Project-Level and Single-Check Detection Rates

While traditional VM-detection baselines remain flat and noisy, AI-environment artifacts provide clear, high-fidelity separation between real workstations and analysis backends.

Contrast of Detection Rates Across Environments

3. The 12 "Class C" Host-Exclusive Artifacts

The authors identified twelve distinct artifacts that were found exclusively on real AI-capable hosts and were completely absent (0%) from every evaluated sandbox backend. These represent the primary evasion indicators.

Class C Host-Exclusive Artifact Surface

4. The Operational Cost Asymmetry of Spoofing

Can defenders simply fake ("spoof") these artifacts? The authors evaluated five cumulative levels of defense spoofing (Table 9):

• L1: Create empty directories (e.g., ~/.claude/)
• L2: Set dummy env vars (e.g., OPENAI_API_KEY=sk-test)
• L3: Open listening dummy ports (e.g., Port 11434)
• L4: Install standard packages (e.g., torch)
• L5: Populate data (e.g., mock weight directories)

While shallow spoofing (L1-L3) artificially inflated the sandbox's project-level detection metrics, it introduces a massive operational cost asymmetry. An adaptive attacker can easily bypass shallow spoofing with lightweight, 3-to-15 Line-of-Code (LoC) structural consistency checks.

For instance, an empty directory check (L1) or verifying if port 11434 actually responds to Ollama HTTP API protocols takes under 3 seconds to execute. To pass these adaptive checks, the defender must run fully functional, populated, and expensive local AI services.

// Example: Adaptive check to distinguish a spoofed port from a real local Ollama server
#include <windows.h>
#include <winsock2.h>
#include <iostream>
#include <string>

#pragma comment(lib, "Ws2_32.lib")

bool VerifyOllamaRealism() {
    WSADATA wsaData;
    if (WSAStartup(MAKEWORD(2, 2), &wsaData) != 0) return false;

    SOCKET ConnectSocket = socket(AF_INET, SOCK_STREAM, IPPROTO_TCP);
    if (ConnectSocket == INVALID_SOCKET) {
        WSACleanup();
        return false;
    }

    sockaddr_in clientService;
    clientService.sin_family = AF_INET;
    clientService.sin_addr.s_addr = inet_addr("127.0.0.1");
    clientService.sin_port = htons(11434);

    // 1. Connection Check
    if (connect(ConnectSocket, (SOCKADDR*)&clientService, sizeof(clientService)) == SOCKET_ERROR) {
        closesocket(ConnectSocket);
        WSACleanup();
        return false; // Port closed -> likely a sterile sandbox
    }

    // 2. HTTP/JSON Protocol Validation (Adaptive Check)
    std::string httpRequest = "GET /api/tags HTTP/1.1\r\nHost: localhost:11434\r\nConnection: close\r\n\r\n";
    send(ConnectSocket, httpRequest.c_str(), (int)httpRequest.length(), 0);

    char recvbuf[1024];
    int bytesReceived = recv(ConnectSocket, recvbuf, sizeof(recvbuf) - 1, 0);
    closesocket(ConnectSocket);
    WSACleanup();

    if (bytesReceived > 0) {
        recvbuf[bytesReceived] = '\0';
        std::string response(recvbuf);
        // A simple port-forwarder or dummy listener won't return valid Ollama JSON metadata
        if (response.find("models") != std::string::npos && response.find("200 OK") != std::string::npos) {
            return true; // Valid Ollama service running
        }
    }
    return false; // Spoofed/fake listener detected!
}

Limitations & Open Questions

• Small Reference Pool: The study compared sandboxes against only three reference hosts. While they represent realistic configurations, a larger enterprise-wide population study is needed to establish whether these indicators are universally reliable across non-developer endpoints.
• Temporal Decay: The AI software ecosystem evolves rapidly. The compiled set of 450 artifacts represents a snapshot from February 2026. If developer tools transition away from local inference servers or change configuration paths, the probes will require frequent maintenance.
• Windows-Centric Evaluation: The current iteration of AIprints focuses exclusively on the Windows 11 guest OS. Evasion behaviors on Linux and macOS—where a substantial portion of AI development occurs—remain unmeasured.

What Practitioners Should Do

If you operate malware analysis sandboxes, threat detection pipelines, or enterprise detection architectures, consider the following actions:

1. Deploy Active Mock Services: Do not just open port 11434 or 8888. Run lightweight, custom Python scripts that respond to HTTP GET requests on /api/tags or Jupyter configuration endpoints with valid JSON schemas mimicking a running service.
2. Populate Realist Caches: Instead of leaving mock folders empty, populate ~/.cache/huggingface and ~/.ollama directories with valid metadata files, structured config JSONs, and small, non-empty files mimicking model configuration states.
3. Format Environment Keys Correctly: If setting mock variables like OPENAI_API_KEY or ANTHROPIC_API_KEY, ensure they use authentic syntax formats (e.g., standard lengths and structural prefixes like sk-proj-) to bypass basic format validation rules.
4. Pre-install Common Packages Globals: Standardize your VM baseline image with common machine learning dependencies (e.g., torch, transformers, langchain) pre-installed inside the global python execution path rather than relying on a completely vanilla, empty OS image.

The Takeaway

As AI tools move from high-end research projects to standard enterprise workstation components, security teams can no longer rely on sterile virtual machines to analyze modern threat vectors. Sandbox evasion has shifted from hiding virtual hardware to exploiting the absence of standard, complex user-space software. To ensure reliable malware detonation, future analysis environments must simulate not just the virtualization layer, but the rich, functional software ecosystems of the modern target user.

Den's Take

This is a brilliantly simple yet alarming shift in evasion tactics. While security teams are busy patching complex hypervisor timing leaks, malware authors are realizing that the easiest way to spot a synthetic sandbox is simply checking if the "developer" host has actually touched any modern AI tools.

The fact that 12 "Class C" host-exclusive artifacts—such as .cursor configurations, the OPENAI_API_KEY environment variable, or an active Ollama listener on port 11434—were found on 0% of the evaluated sandboxes is a damning indictment of current threat intelligence pipelines. It costs an unprivileged user-mode adversary virtually nothing to query these via standard Win32 APIs like PathFileExistsW on Windows 11, yet it completely breaks detection on platforms like CAPEv2 and ANY.RUN.

If our sandbox environments don't start realistically simulating these rich, local AI development ecosystems, they will become completely blind to modern, environment-aware malware. The defense community needs to move past static "wear-and-tear" checks and begin dynamically mocking developer-class AI workflows.