Security researchers at Varonis identified a critical vulnerability in Microsoft 365 Copilot that allowed attackers to exfiltrate user passwords and other sensitive information without explicit user consent. The vulnerability centered on an undocumented prompt parameter that the researchers discovered through a systematic questioning approach directed at Copilot itself.
The researchers sought to create an exploit that would trigger data theft when users clicked a malicious link. When Copilot initially refused to assist with such requests, the researchers used each refusal as an opportunity to extract technical details about the system’s safety mechanisms. Through iterative questioning about guardrails, URL structures, and prompt execution, Copilot eventually disclosed the existence of an undocumented parameter called ?autorun=1. When combined with the known ?q= parameter, this string allowed prompts to execute automatically upon link activation without requiring user interaction.
Utilizing this parameter, researchers crafted URLs that could be distributed via email or messaging platforms. When a target clicked such a link, Copilot would search the user’s inbox for sensitive data, extract email addresses and passwords, and transmit the information to attacker-controlled servers. The exfiltrated data was encoded in base64 format to prevent detection and transmission errors. In addition to this attack vector, Varonis demonstrated how prompt injection embedded in webpages could poison Copilot’s permanent memory store, allowing attackers to modify user preferences and instructions that persist across sessions.
Microsoft addressed the initial vulnerability in February following Varonis’s report, disabling the ?q= parameter’s ability to inject text automatically. More comprehensive fixes were implemented subsequently. Microsoft stated that customers are protected without requiring manual intervention and that the company continuously updates safeguards against similar techniques. The incidents underscore broader security concerns around AI assistants and reinforce recommendations that users exercise caution with unexpected links and monitor AI interactions for unusual outputs.
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