Microsoft Copilot Root Access Vulnerability Explained (July 2025)
In July 2026, a serious vulnerability in Microsoft Copilot Enterprise was uncovered that allowed attackers to gain unauthorized root access to its backend container. The flaw originated from a misconfigured Python sandbox powered by Jupyter Notebook, where a malicious script disguised as pgrep exploited insecure environment variables and privilege mismanagement. This incident, disclosed by Eye Security, reveals the critical risks in AI-based sandboxes and underlines the importance of secure container configurations in enterprise AI tools. Microsoft patched the issue, but the case remains a benchmark in AI infrastructure vulnerabilities.
Quick answer: In 2025, Eye Security reported that the Python sandbox added to Microsoft Copilot Enterprise let them gain root inside the sandbox container through a PATH hijack: a root-run script called a command without its full path, so a planted file with the same name ran instead. Microsoft fixed it and rated it moderate. It shows why AI code sandboxes need the same hardening as any server.
Key takeaways
- The flaw was inside a sandbox container used for Copilot's Python feature, not a takeover of Microsoft 365 or customer data.
- The reported cause was a classic Linux PATH hijack: a root script ran a command by bare name from a writable location.
- Microsoft fixed it after disclosure and, as reported, treated it as moderate severity because the container was isolated.
- The lesson is general: any feature that lets users run code needs a minimal, read-only, unprivileged sandbox.
- Claims that circulated online about full compromise went further than the researchers' own findings.
- Details here come from public write-ups. Read the Eye Security report for primary information.
What happened?
Security firm Eye Security published research in July 2025 about Microsoft Copilot Enterprise. After an update in April 2025 added a live Python sandbox, which behaves like the code-interpreter features in other AI assistants, the researchers found that commands entered through the sandbox could run, and that they could reach root privileges inside the container that hosted it. Microsoft was told, fixed the problem, and public reports said it rated the issue as moderate in severity.
It helps to be precise about what this was not. It was not a breach of customer tenants or Microsoft 365 data. Root inside a container is a serious finding for the container's security model, but the sandbox was built to be isolated from other systems, and the researchers did not report escaping it to reach customer data. Headlines that said Copilot was "hacked" or "rooted" were broader than that.
What was the technical weakness?
The reported cause is a well-known class of Linux bug called a PATH hijack, or untrusted search path. In short:
- The sandbox ran user-supplied code as a low-privilege user, which is normal.
- A service script ran as root and, inside it, called a helper command such as
pgrepby name only, without its full path. - When a script calls a command by bare name, the shell searches the directories listed in the
PATHenvironment variable in order. - If a directory that the low-privilege user can write to appears earlier in that search order, a file placed there with the same name runs instead of the real command, and it runs with the root script's privileges.
This is a generic pattern, not a Copilot-only trick. The general defences are listed in the table below. We are deliberately not reproducing a step-by-step exploit, because the lesson is how to prevent it. The primary account is on the Eye Security research site, and the vulnerability class is catalogued as CWE-426, Untrusted Search Path.
How do you prevent this class of bug?
| Control | Why it helps |
|---|---|
| Use absolute paths in privileged scripts | Removes the search through PATH entirely |
| Set a fixed, minimal PATH in root scripts | Prevents user-writable directories being searched first |
| Do not run services as root | A hijack then gains little |
| Read-only root filesystem, no writable directories in PATH | Stops planting files that get executed |
Mount user storage with noexec | User uploads cannot be run as programs |
| Drop capabilities, use seccomp and a sandbox such as gVisor or a microVM | Limits what a compromised container can do |
| Static checks in CI | Linters can flag bare command names in privileged scripts |
Why does it matter for AI products?
AI assistants increasingly run code, browse pages and call tools on a user's behalf. Each feature widens the attack surface. A code sandbox is effectively a small hosting service, and it needs the same engineering as any multi-tenant platform. Also remember that prompts can come from untrusted content, so a document or web page could instruct the model to run code. The OWASP Top 10 for LLM applications covers risks such as insecure plugin design and excessive agency, and MITRE ATLAS maps adversary techniques against AI systems.
What should builders and security teams do?
- Builders: run user code in a locked-down, unprivileged, network-restricted environment; review every startup script that runs as root; test with a threat model that assumes the user controls the code.
- Customers: read the vendor's security documentation, limit who may use code execution features, and log their use.
- Testers: sandbox testing needs written authorisation from the owner. Under India's IT Act, probing a vendor's production service without permission is an offence, even if you intend to report it. Use the vendor's coordinated disclosure or bug bounty rules.
For related privilege escalation write-ups on this site, see AWS Client VPN for Windows CVE-2025-8069, the Palo Alto GlobalProtect privilege escalation and the AI vibe coding platform logic flaw.
Next steps
To learn to assess AI applications safely, see WebAsha's AI and LLM security course.
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Vaishnavi is a skilled tech professional at the Ethical Hacking Training Institute in Pune, responsible for managing and optimizing the technical infrastructure that supports advanced cybersecurity education. With deep expertise in network security, backend operations, and system performance, she ensures that practical labs, online modules, and assessments run smoothly and securely. Her behind-the-scenes contributions play a vital role in delivering a seamless and secure learning experience for aspiring ethical hackers.
