Google Gemini Vulnerability Exposes Private Calendar Data Through Malicious Invites

Researchers have uncovered a security vulnerability in Google Gemini that exploits indirect prompt injection to bypass Google Calendar’s privacy controls. This flaw allows attackers to embed malicious prompts within calendar invites, enabling them to access private meeting data. The attack is initiated when a user queries Gemini about their schedule, prompting the AI to process the malicious payload hidden in the invite’s description.

Once activated, Gemini can create a new calendar event summarizing the user’s private meetings, which may be visible to the attacker in certain enterprise configurations. This means that sensitive information can be exfiltrated without any direct action from the target user. The vulnerability highlights the potential risks associated with AI-driven features and the need for stringent security measures.

The issue was responsibly disclosed and has since been addressed, but it underscores the evolving nature of cyber threats as AI technologies become more integrated into daily workflows. As organizations increasingly adopt AI tools, they must remain vigilant about the security implications of these technologies.

Implications for Users and Organizations

This incident serves as a reminder of the importance of scrutinizing AI applications and their interactions with sensitive data. Users should be aware that even seemingly innocuous interactions with AI can lead to data exposure if vulnerabilities exist.

Organizations should conduct regular audits of their AI systems and ensure that adequate controls are in place to prevent unauthorized access and data leakage. This includes reviewing permissions for calendar events and ensuring that security measures are robust enough to handle potential exploitation scenarios.

Additionally, the findings from this vulnerability align with other recent discoveries, such as the Reprompt attack, which could allow adversaries to exfiltrate sensitive data from AI chatbots with minimal effort. This pattern emphasizes the need for continuous evaluation of AI systems across various security dimensions.

• CVE-2026-0612: A vulnerability in The Librarian AI tool that allows access to internal infrastructure.
• CVE-2026-22708: A critical vulnerability in Cursor enabling remote code execution via indirect prompt injection.
• Reprompt attack: A method to exfiltrate data from AI chatbots like Microsoft Copilot.
• Malicious plugins for Anthropic Claude Code that bypass security protections.
• Vulnerabilities in coding IDEs that fail to handle SSRF issues and enforce authorization.