Security researchers identified a critical vulnerability in Microsoft 365 Copilot that could be exploited to exfiltrate data without user interaction. The flaw was discovered by prompting the AI to explain its own security guardrails, eventually revealing a hidden parameter that bypassed mandatory confirmation requirements.
An Unusual Discovery Process
The vulnerability discovery process stood out for its unconventional methodology. Rather than utilizing standard reverse engineering or traditional penetration testing tools, the researchers at Varonis treated the Microsoft 365 Copilot engine like a participant in a game of '20 questions.' By intentionally querying the AI about its own safety protocols and the limitations of its user-confirmation guardrails, the researchers effectively social-engineered the model into revealing its internal mechanics. Each refusal from the AI served as a feedback loop, providing incremental data about how the system handled sensitive prompts and deep links. Ultimately, the model revealed the existence of undocumented parameters designed to facilitate automated workflows, which allowed the team to pinpoint a specific mechanism that could be abused to bypass established security consent requirements.
The Mechanics of the Exploitation
At the heart of the security concern was the '?autorun=1' string, an undocumented parameter within Copilot. When combined with the widely recognized '?q=' parameter—typically used for text injection—this allowed for the execution of commands without the explicit user gesture usually required by the platform. In a standard secure configuration, Copilot necessitates a specific user action, such as pressing a keyboard key, to approve high-risk or automated commands. However, by using these parameters in a specially crafted URL, an attacker could force the system to perform operations the moment a target user clicked a link. This enabled the potential for data exfiltration, as the exploit could trigger the AI to process unauthorized prompts silently and instantaneously, completely circumventing the layer of human verification intended to protect the enterprise environment.
Mitigation and Response
Microsoft began addressing the vulnerability shortly after receiving the report from Varonis. Initially, the tech giant implemented a silent mitigation in February 2026, approximately three months following the disclosure. This interim fix involved restricting the functionality of the '?q=' parameter, preventing it from automatically injecting text into the chatbot's input field. By forcing users to manually click and type, Microsoft eliminated the ability for third-party browser integrations to leverage the parameter for automated attacks. Following this initial step, the company rolled out more extensive and comprehensive fixes on Tuesday, August 18, 2026, to fully secure the platform against the identified exploit path and ensure that the AI assistant's guardrails remain robust against similar future manipulation.
⚖ The Balanced View
Supporting view
The Varonis researchers demonstrated that even high-tier AI models possess internal logic that can be systematically interrogated, potentially providing a roadmap for security hardening.
Concerns & criticism
The ability to extract trade secrets and bypass critical safety controls simply by conversing with an AI model highlights significant risks inherent in the current design of LLM-based enterprise assistants.
→What's next
Microsoft will likely continue to monitor for similar prompt-based bypasses as AI-driven automation becomes more prevalent in enterprise software. Security researchers are expected to further explore the boundaries of large language model internal architectures to identify other undocumented features that could pose potential security risks.
