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Introduction:
When Microsoft 365 Copilot Cowork needs to generate an Excel file, it bypasses Microsoft’s own Excel entirely and relies on LibreOffice running on Linux. This reveals a hidden dependency on open-source components within Microsoft’s AI cloud stack, raising critical questions about supply chain security, vendor lock-in, and the true architecture of “enterprise-grade” AI assistants.
Learning Objectives:
- Understand how AI agents like Copilot Cowork depend on open-source libraries (openpyxl, LibreOffice) and Linux containers for document generation.
- Analyze security and compliance risks introduced by using community tools inside proprietary cloud environments.
- Learn practical commands and techniques to audit, replicate, and harden such mixed-tool AI architectures.
You Should Know
- Unpacking the .xlsx: How to Spot LibreOffice in the Wild
Every Excel file created by Copilot Cowork contains an internal `app.xml` file that reveals the generating application. Because `.xlsx` files are ZIP archives, you can inspect them with simple command-line tools.
Step‑by‑step guide:
- Obtain a sample `.xlsx` file generated by Copilot Cowork (or any suspicious Excel file).
- Use `unzip` (Linux/macOS) or expand the archive manually on Windows:
– Linux/macOS: `unzip suspicious.xlsx -d extracted/`
– Windows (PowerShell): `Expand-Archive -Path suspicious.xlsx -DestinationPath extracted`
3. Navigate to `extracted/docProps/` and open `app.xml` with a text editor or cat:
cat extracted/docProps/app.xml | grep -i "libreoffice|application"
4. Look for lines like `<AppVersion>25.8.4.2</AppVersion>.
5. Security relevance: This detection method helps forensic analysts identify AI‑generated documents and discover unauthorized use of non‑standard toolchains in enterprise environments.
2. Linux Container Forensics: Detecting Headless LibreOffice
Since Copilot Cowork runs inside a Linux container (likely a Kubernetes pod in Azure), security teams can hunt for signs of `soffice` (LibreOffice headless mode) or `openpyxl` usage through process monitoring and filesystem scans.
Step‑by‑step guide:
- On a Linux host suspected of running a similar AI agent:
Find running LibreOffice processes ps aux | grep -i soffice Search for openpyxl imports in Python environments grep -r "import openpyxl" /opt/agent/ 2>/dev/null Check installed Python packages pip list | grep openpyxl
-
Container‑specific commands (run inside the container or via
docker exec):Identify the Linux distribution cat /etc/os-release List all binaries related to document conversion which soffice || which libreoffice
- Windows (WSL or remote access): Use `wsl` to run the above Linux commands if auditing a hybrid environment.
- Why this matters: Attackers often abuse headless office suites to generate malicious documents (e.g., macro‑enabled files) or to exfiltrate data through conversion engines. Detecting unexpected `soffice` instances can reveal compromised AI containers.
3. API Security & the “Private Exclave” Risk
Kevin King’s comment mentions a “private exclave in Anthropic” where Microsoft hosts parts of Copilot. This architecture means API calls flow between Microsoft Azure and Anthropic’s infrastructure, introducing potential API security blind spots.
Step‑by‑step hardening for API‑first AI systems:
- Validate API endpoints: Use `curl` to test for unexpected open ports or misconfigured gateways (example for testing a suspected AI proxy):
curl -X GET https://your-copilot-endpoint/v1/health -H "Authorization: Bearer $TOKEN" -v
- Implement mutual TLS (mTLS) between your cloud and any third‑party enclave.
- Audit API logs for anomalies: Commands to search for unusual user agents or IPs in Azure API Management logs:
az monitor activity-log list --query "[?contains(properties.body, 'LibreOffice')]" --output table
- Best practice: Restrict which libraries and binaries your AI agents can invoke using eBPF or seccomp profiles on Linux containers.
4. Cloud Hardening for Mixed‑Tool Environments
When a cloud AI service uses open‑source components not originally designed for that cloud (like LibreOffice on Azure), traditional hardening must expand to cover software bill of materials (SBOM) and runtime controls.
Step‑by‑step guide to harden your own AI workloads:
- Generate an SBOM of every container image used by your AI agent:
docker sbom your-ai-image:latest > sbom.json
- Enforce read‑only root filesystems in Kubernetes to prevent tampering with `soffice` or Python libraries:
securityContext: readOnlyRootFilesystem: true
- Use Azure Policy (or Open Policy Agent) to block containers that run unexpected binaries like `soffice` unless explicitly approved.
- Linux command to monitor file access to sensitive document directories:
inotifywait -m -r /var/ai_workspace -e open,modify,delete
5. Windows equivalent (PowerShell monitoring):
$watcher = New-Object System.IO.FileSystemWatcher
$watcher.Path = "C:\AI\workspace"
Register-ObjectEvent $watcher "Changed" -Action { Write-Host "File changed" }
5. Practical Lab: Replicating the Copilot Excel Workflow
You can recreate the same LibreOffice‑based Excel generation locally for testing and learning.
Step‑by‑step tutorial:
1. Set up a Linux environment (Ubuntu 22.04+):
sudo apt update && sudo apt install -y libreoffice-headless python3-pip pip3 install openpyxl
2. Create a Python script (generate_excel.py) that uses openpyxl to build an `.xlsx` file:
import openpyxl
from openpyxl import Workbook
wb = Workbook()
ws = wb.active
ws['A1'] = "Generated by Copilot Cowork (simulated)"
wb.save("output.xlsx")
3. Validate the file with headless LibreOffice (as Microsoft does):
soffice --headless --convert-to xlsx --outdir /tmp output.xlsx
4. Unpack and inspect `app.xml` to confirm the LibreOffice signature appears.
5. Security extension: Add a step to scan the generated file with ClamAV before delivering it:
clamscan --detect-pua=yes output.xlsx
This mimics a secure AI pipeline that validates all generated content.
- Mitigating Supply Chain Risks from Open Source in AI Pipelines
The use of openpyxl and LibreOffice introduces dependencies that must be tracked for vulnerabilities (e.g., CVE‑2024‑xxx in LibreOffice’s XML parser).
Step‑by‑step risk mitigation:
- Automate vulnerability scanning for Python packages and Linux binaries:
For Python safety check --json > openpyxl_vulns.json For system packages (Debian/Ubuntu) apt list --installed | grep -i libreoffice && apt-get changelog libreoffice | grep -i cve
- Pin exact versions in your AI agent’s requirements.txt and container base image tags.
- Set up a private mirror for PyPI and apt repositories to control which versions are allowed.
- Windows administrators can use WSL or Docker Desktop to run the same scans against a local Linux test container.
- Audit the entire AI pipeline with a command like:
trivy image your-ai-image:latest --severity HIGH,CRITICAL
7. Windows Commands for Auditing AI Workloads Remotely
Even though the AI agent runs on Linux, Windows‑based security teams can audit it through Azure CLI, SSH, or PowerShell remoting.
Useful commands:
- List all Azure containers that might host Copilot‑like workloads:
az container list --query "[?contains(image, 'copilot')]" -o table
- SSH into a Linux container from Windows and search for LibreOffice artifacts:
ssh user@container-ip "find / -name 'soffice' 2>/dev/null"
- Collect forensic evidence from a running pod (using kubectl):
kubectl exec -it copilot-pod -- cat /proc/version kubectl cp copilot-pod:/opt/agent/output.xlsx C:\forensics\
- Use Windows Event Logs to detect when a user downloads an AI‑generated Excel file (e.g., via Microsoft 365 audit logs):
Search-UnifiedAuditLog -Operations "FileDownloaded" -StartDate (Get-Date).AddDays(-1)
What Undercode Say:
- Key Takeaway 1: Microsoft’s Copilot Cowork relies on open‑source LibreOffice and Python’s openpyxl inside Linux containers – a pragmatic but unexpected choice that breaks the illusion of a “pure Microsoft” stack.
- Key Takeaway 2: Security professionals must expand their threat models to include AI agents invoking common Linux tools (soffice, pandoc, ImageMagick) which can become attack vectors or evidence of compromise.
- Key Takeaway 3: API security and supply chain visibility are no longer optional; SBOMs, container hardening, and runtime monitoring of binaries like LibreOffice are essential for cloud AI workloads.
Analysis: The revelation that Microsoft uses LibreOffice to generate Excel files is both technically sensible and organizationally ironic. It highlights a growing trend: even top-tier cloud providers assemble AI services from off‑the‑shelf open‑source components rather than building everything from scratch. For defenders, this means legacy threats (e.g., malicious XLSX files crafted by soffice) can now originate from trusted SaaS AI. Expect attackers to weaponize this by poisoning open‑source dependencies that AI agents implicitly trust. Red teams should immediately test whether their target’s AI assistants can be tricked into generating malicious documents through manipulated training data or environment variables. Finally, the move toward API‑first architectures will force security teams to monitor inter‑cloud traffic (Azure ↔ Anthropic) with unprecedented granularity.
Prediction:
Within 12 months, we will see the first major supply chain attack against an enterprise AI assistant that abuses common open‑source libraries like openpyxl or headless LibreOffice. Microsoft and other vendors will be forced to publish detailed SBOMs for all AI components, and regulatory frameworks (e.g., EU AI Act) will explicitly require runtime validation of third‑party binaries in “high‑risk” AI systems. Simultaneously, the reliance on Linux containers by Microsoft Copilot will accelerate cross‑platform security tooling, making Windows–Linux hybrid forensics a standard skill for SOC analysts. The era of “vendor‑native everything” is over – AI is democratizing software dependencies, whether the giants like it or not.
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