This article is for Google Workspace administrators. Gemini users: Learn how Google protects you when you use the Gemini app or Gemini in Workspace apps: Gmail, Docs editors, Drive, and Chat.
Indirect prompt injections are a sophisticated security vulnerability in generative AI systems. This article explains Google's comprehensive, layered defense strategy for mitigating this vulnerability in the Gemini app and Gemini in Workspace apps.
On this page
- What is a prompt in the context of generative AI?
- What is an indirect prompt injection?
- How do indirect prompt injections operate?
- What are some real-world examples of indirect prompt injection attacks?
- Why are indirect prompt injections a significant concern? What’s the risk?
- What is Google's approach to mitigating indirect prompt injection attacks?
- What are the key layers of defense against indirect prompt injections?
- How do prompt injection content classifiers work?
- What is security thought reinforcement?
- How does markdown sanitization and suspicious URL redaction enhance security?
- What is the user confirmation framework?
- Why are end-user security mitigation notifications important?
What is a prompt in the context of generative AI?
A prompt is an instruction or input given to a generative AI model to guide its output. Generative AI models interpret these prompts to create content, such as text, images, or code, based on patterns learned from vast datasets.
What is an indirect prompt injection?
An indirect prompt injection is a type of security vulnerability in AI systems where malicious instructions are hidden in external data that the AI model processes. These instructions are not given directly to AI by the user. The goal is to manipulate the system's behavior or output without the user's explicit knowledge.
How do indirect prompt injections operate?
Indirect prompt injections operate when an AI system processes external data, such as website content, email, or documents, that contain embedded malicious instructions. The system, unaware of the hidden commands or malicious instructions, executes them along with its primary task. This can lead to unintended actions or information disclosure.
What are some real-world examples of indirect prompt injection attacks?
- Chatbot hijacked—An AI chatbot trained on external data is fed a malicious instruction on a web page, causing it to reveal sensitive internal information.
- Summarizer compromised—An AI system summarizes a document containing hidden instructions and performs an unauthorized action, like sending an email.
- Data exfiltration—An AI system is asked to process an infected file and inadvertently extracts and sends confidential data to an external destination.
Why are indirect prompt injections a significant concern? What’s the risk?
Indirect prompt injections pose a significant threat to AI system security and data privacy. They can lead to unauthorized data access, manipulation of AI behavior, and potential misuse of information. This vulnerability undermines the trustworthiness of AI, creating pathways for cyberattacks that are difficult to detect and prevent through traditional security measures.
What is Google's approach to mitigating indirect prompt injection attacks?
Google employs a comprehensive, layered security approach to mitigate indirect prompt injection attacks, particularly with Gemini. This strategy introduces security measures designed for each stage of the prompt lifecycle, from model hardening to purpose-built machine-learning models and system-level safeguards.
Since the initial deployment of our enhanced indirect prompt injection defenses, our layered protections have consistently mitigated indirect prompt injection attempts and adapted to new attack patterns. Our ongoing monitoring and rapid response capabilities ensure we continuously learn from every interaction, strengthening our defenses.
What are the key layers of defense against indirect prompt injections?
Google's layered security approach includes:
- Prompt injection content classifiers—Proprietary machine-learning models that detect malicious prompts and instructions within various data formats.
- Security thought reinforcement—Targeted security instructions that are added around the prompt content. These instructions remind the LLM (large language model) to perform the user-directed task and ignore adversarial instructions.
- Markdown sanitization and suspicious URL redaction—Identifying and redacting external image URLs and suspicious links using Google Safe Browsing to prevent URL-based attacks and data exfiltration.
- User confirmation framework—A contextual system that requires explicit user confirmation for potentially risky operations, such as deleting calendar events.
- End-user security mitigation notifications—Contextual information provided to users when security issues are detected and mitigated. These notifications encourage users to learn more via dedicated help center articles.
- Model resilience—The adversarial robustness of Gemini models, which protects them from explicit malicious manipulation.
How do prompt injection content classifiers work?
Prompt injection content classifiers serve as an initial defense by identifying and flagging suspicious inputs that might contain malicious instructions. These classifiers analyze the structure, keywords, and patterns within prompts to detect potential injection attempts before they can impact the AI model's behavior, filtering out harmful content.
What is security thought reinforcement?
Security thought reinforcement involves training AI models to prioritize security considerations in their decision-making processes. This technique adds targeted security instructions surrounding the prompt content to remind the LLM to stay focused on the user-directed task and ignore any adversarial or malicious instructions embedded in the content.
How does markdown sanitization and suspicious URL redaction enhance security?
Markdown sanitization removes potentially harmful code or scripting elements hidden within markdown-formatted text, preventing their execution. Suspicious URL redaction identifies and masks links that point to known malicious websites, stopping the AI system from accessing or propagating dangerous content. This prevents indirect prompt injections that exploit formatting vulnerabilities or redirect AI to malicious external resources.
What is the user confirmation framework?
The user confirmation framework introduces an explicit approval step for sensitive AI-generated actions or outputs. Before executing potentially harmful commands or sharing confidential information, the AI system prompts the user to confirm their intent. This human-in-the-loop (HITL) approach acts as a final safeguard against unauthorized or unintended actions resulting from a successful prompt injection attack.
Why are end-user security mitigation notifications important?
End-user security mitigation notifications inform users when a potential security risk has been detected or mitigated within an AI system. These alerts provide transparency about the security measures taken and educate users on potential threats, empowering them to make informed decisions. This fosters a collaborative approach to AI security, reinforcing trust and encouraging safer interaction with AI applications.
Additional resources
For more information on Google's progress and research on generative AI threat actors, attack techniques, and vulnerabilities, go to Mitigating prompt injection attacks with a layered defense strategy.
