Google Gemini exploited to leak private Calendar data through malicious invites
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Gemini AI assistant tricked into leaking Google Calendar data
Using only natural language instructions, researchers were able to bypass Google Gemini's defenses against malicious prompt injection and create misleading events to leak private Calendar data. Sensitive data could be exfiltrated this way, delivered to an attacker inside the description of a
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This Gemini Calendar trick turns a simple invite into a privacy nightmare
Google was duly notified and has added new protections, but the issue highlights how AI features can be abused through natural language. Google recently made Gemini a lot more useful by letting it work across multiple Calendars, not just your primary one. You can now ask about events or create new
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Researchers got Gemini AI to leak Google Calendar data, they claim
Google's Gemini AI assistant can be coerced into sharing private user data, according to researchers. Credit: Mateusz Slodkowski/SOPA Images/LightRocket via Getty Images Google's AI assistant Gemini has surged to the top of AI leaderboards since the search giant's latest update last
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A Google Gemini security flaw let hackers use calendar invites to steal private data
Security researchers found yet another way to run prompt injection attacks on Google's Gemini AI, this time to exfiltrate sensitive Google Calendar data. Prompt injection is a type of attack in which the malicious actor hides a prompt in an otherwise benign message. When the victim tells their AI
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Indirect prompt injection in Google Gemini enabled unauthorized access to meeting data - SiliconANGLE
Indirect prompt injection in Google Gemini enabled unauthorized access to meeting data A new report out today from cybersecurity company Miggo Security Ltd. details a now-mitigated vulnerability in Google LLC's artificial intelligence ecosystem that allowed for a natural-language prompt injection
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Miggo Security bypasses Google Gemini defenses via calendar invites
Researchers bypassed Google Gemini's defenses to exfiltrate private Google Calendar data using natural language instructions. The attack created misleading events, delivering sensitive data to an attacker within a Calendar event description. Gemini, Google's large language model (LLM) assistant,
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Security researchers at Miggo Security discovered how to trick Google Gemini into leaking sensitive Calendar information using only a calendar invite and natural language instructions. The attack exploited Gemini's deep integration with Google Workspace, bypassing existing defenses to exfiltrate private meeting data without user awareness. Google has since added new mitigations, but the incident highlights ongoing challenges in securing AI assistants against prompt injection attacks.
Google Gemini Vulnerability Exposes Private Calendar Information
Security researchers at Miggo Security have revealed a critical vulnerability in Google Gemini that allowed attackers to leak Google Calendar data through a sophisticated prompt injection attack. The AI security flaw exploited the assistant's deep integration with Google Workspace apps, demonstrating how natural language instructions could bypass existing defenses and grant unauthorized access to meeting data
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Source: Android Authority
The attack required nothing more than a calendar invite. Miggo Security researchers embedded carefully crafted prompts into the description field of a Google Calendar event, which remained dormant until activated by a routine user query
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. When victims asked Google Gemini simple questions about their schedule, the AI assistant would parse all calendar entries, including the malicious calendar invites, and execute the hidden instructions without raising security warnings.How the Attack Exploited AI Assistant Integration
The exploit unfolded in three distinct stages. First, an attacker sent a calendar invite containing a payload disguised as benign text that instructed Gemini to summarize private meetings, create a new event, and store sensitive meeting summaries in the event description
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. The instructions appeared harmless in isolation, which allowed them to evade Google's separate model designed to detect malicious prompts in the primary Gemini assistant1
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Source: SiliconANGLE
In the second stage, the payload remained inactive until the victim asked Gemini about their schedule. This triggered the exfiltration activity, causing Google's AI assistant to load and interpret all relevant events
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. Finally, Gemini executed the embedded instructions, creating a new calendar event with a full summary of the user's private meetings while responding to the victim with an innocuous message like "it's a free time slot"3
.Enterprise Configurations Amplify Data Privacy Risks
In many enterprise setups, the newly created event containing sensitive meeting summaries became visible to event participants, directly granting the attacker access to confidential information without any direct user interaction
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. This vulnerability in large language models highlights a fundamental challenge: AI assistants cannot distinguish between legitimate instructions and data used to execute those instructions4
.Miggo's head of research, Liad Eliyahu, told BleepingComputer that the attack demonstrates how Gemini's reasoning capabilities remained vulnerable to manipulation despite Google implementing additional defenses following a previous SafeBreach report in August 2025
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. That earlier incident also involved malicious Google Calendar invites being used to take control of Gemini's agents and leak sensitive user data.Related Stories
Mitigations and the Future of Application Security
Google confirmed the findings and has since added new mitigations to block such attacks
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. However, the incident underscores broader challenges in cybersecurity as AI systems become more deeply integrated into enterprise workflows. The researchers argue that application security must evolve from syntactic detection to context-aware defenses that can reason about semantics and attribute intent5
.Miggo Security researchers emphasize that effective protection will require runtime systems that track data provenance and treat large language models as full application layers with privileges that must be carefully governed
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. As AI assistants gain more capabilities across multiple services, the complexities of foreseeing new exploitation models driven by natural language with ambiguous intent will continue to challenge traditional security frameworks1
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