Elizabeth Montalbano, Contributing Writer January 20, 2026 4 Min Read Source: Deemerwha Studio via Shutterstock Researchers have uncovered a prompt injection vulnerability in Google's application ecosystem that allows attackers to gain access to sensitive data via its Gemini generative artificial intellience (GenAI) tool. The flaw is the latest one that various researchers have discovered in Gemini and other AI assistants that demonstrate how large language model (LLM)-driven apps have created new avenues for exploit. The flaw allows attackers to place a dormant payload inside a standard Google Calendar invite that can bypass the scheduling app's privacy controls, security firm Miggo revealed in a blog post published Monday. Ultimately, Miggo researchers used the bypass to access private meeting data as well as to create their own deceptive calendar events without any interaction on the part of the targeted user. The flaw stems from Gemini's integration into Google Calendar; the AI service functions as an assistant that scans a user's calendar events to help them organize their schedule, answering prompt questions such as, "What is my schedule today?" using live details from the calendar. "The mechanism for this attack exploits that integration," Miggo head of research Liad Eliyahu explained in the post. "Because Gemini automatically ingests and interprets event data to be helpful, an attacker who can influence event fields can plant natural language instructions that the model may later execute." Gemini Flaw Reveals Structural Limitation The flaw itself is of the prompt injection variety, which is when someone manipulates user inputs into an LLM to change the model's behavior or outputs in unintended ways. Miggo researchers discovered its existence based on a hypothesis that if an attacker could control the description field of an event on a user's calendar, then they could place a prompt that Gemini would execute, which they proved, ultimately, to be true. What makes the discovery of the flaw particularly notable, however, is that it demonstrates a "structural limitation" in how AI-integrated products reason about user intent within language-based AI, Eliyahu said. Although Google already has mechanisms in place in Gemini to detect malicious prompts, the researchers were able to exploit the flaw through natural language. "The takeaway is clear: AI native features introduce a new class of exploitability," Eliyahu wrote. "AI applications can be manipulated through the very language they're designed to understand. Vulnerabilities are no longer confined to code. They now live in language, context, and AI behavior at runtime." Weaponizing Google Calendar Invites The attack chain starts with the creation of a new calendar event, which is sent to a targeted user. In the event description field, the researchers included the embedded prompt-injection payload in the form of instructions to the Gemini AI to take certain steps if the person who created the event asks about the event or any other entry on the calendar. Those instructions tell Gemini to: summarize all the users meetings for a specific day (including private ones); exfiltrate this data by writing it into the description of a new calendar event; and masquerade the action by giving the user a harmless response ("it's a free time slot"). Though the payload was "syntactically innocuous, meaning it was plausible as a user request," it turns out to be semantically harmful when executed with the model tool's permissions, Eliyahu wrote. This happens with a trigger in the form of asking Gemini a routine question about the user's schedule (e.g., "Hey Gemini, am I free on Saturday?"). In response to this query, Gemini will load and parse all relevant calendar events, including the malicious one, activating the payload. This process happens without alerting the end user that anything is amiss, with Gemini behaving normally and replying that the specific time requested is a free time slot. Behind the scenes is a different story, however, with Gemini creating a new calendar event with a full summary of the target user's private meetings in the event's description. "In many enterprise calendar configurations, the new event was visible to the attacker, allowing them to read the exfiltrated private data without the target user ever taking any action," Eliyahu wrote. Improving Prompt Injection Defenses Overall, the vulnerability and attack flow demonstrate why securing LLM-powered applications is a complex challenge that requires defenders to go beyond the syntactic nature of traditional application security (AppSec), Eliyahu wrote. Traditionally in AppSec, threat hunters look for high-signal strings and patterns, such as SQL payloads, script tags, or escaping anomalies, and block or sanitize them, he said. In contrast, vulnerabilities in LLM-powered systems are semantic, and even seemingly innocuous language strings can become a threat due to their context, intent, and the model's ability to act. "This shift shows how simple pattern-based defenses are inadequate," Eliyahu wrote. "Attackers can hide intent in otherwise benign language, and rely on the model's interpretation of language to determine the exploitability." To defend against the threats that modern LLM-based systems pose, defenders must evolve beyond keyword blocking to developing runtime systems that reason about semantics and attribute intent , as well as track data provenance, according to Eliyahu. "In other words, it must employ security controls that treat LLMs as full application layers with privileges that must be carefully governed," he wrote. This, in turn, he added, will require an interdisciplinary effort that combines model-level safeguards, robust runtime policy enforcement, developer discipline, and continuous monitoring to close the semanic gap in GenAI solutions that attackers are now exploiting. About the Author Elizabeth Montalbano, Contributing Writer Elizabeth Montalbano is a freelance writer, journalist, and therapeutic writing mentor with more than 25 years of professional experience. Her areas of expertise include technology, business, and culture. Elizabeth previously lived and worked as a full-time journalist in Phoenix, San Francisco, and New York City; she currently resides in a village on the southwest coast of Portugal. In her free time, she enjoys surfing, hiking with her dogs, traveling, playing music, yoga, and cooking. See more from Elizabeth Montalbano, Contributing Writer