Google's Gemini large language model (LLM) has been flagged for vulnerabilities that could potentially compromise system prompts, generate harmful content, and enable indirect injection attacks.
The discoveries come from HiddenLayer, which highlighted that these issues could affect users utilizing Gemini Advanced with Google Workspace and organizations leveraging the LLM API.
One vulnerability involves bypassing security measures to leak system prompts, which provide instructions to the LLM for generating appropriate responses. By manipulating the model to output foundational instructions in a markdown block, attackers could exploit this vulnerability.
Another class of vulnerabilities concerns "crafty jailbreaking" techniques, enabling Gemini models to produce misinformation, such as election-related falsehoods or instructions for illegal activities, by prompting the model to enter a fictional state.
Additionally, a third weakness allows for information leakage in system prompts by inputting repeated uncommon tokens, fooling the LLM into responding with confirmation messages containing prompt information.
A further test scenario involves utilizing Gemini Advanced in conjunction with a specially crafted Google document linked to the LLM via the Google Workspace extension. This setup could potentially enable attackers to override model instructions and execute malicious actions, granting them control over user interactions with the model.
Although these vulnerabilities are not unique to Gemini and are prevalent in other LLMs, they underscore the importance of testing models for prompt attacks and other adversarial behaviors.
In response, Google has stated its commitment to safeguarding users from such vulnerabilities through red-teaming exercises and model training. Additionally, the company is implementing restrictions on election-related queries to prevent the dissemination of misleading or harmful information.
While these measures demonstrate Google's efforts to enhance security, ongoing vigilance and proactive mitigation remain crucial in safeguarding against emerging threats in language model technologies.
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