Generative AI Fundamentals Accreditation: What is Prompt Injection and How to Prevent It for AI Models

Learn what prompt injection is and how it can be used to manipulate the behavior or output of AI models, especially large language models, and how to protect your models from this security issue.

Single Choice Question

A user intentionally crafts instructions to manipulate the normal behavior of an AI model in an attempt to extract confidential information from the model. What is the term used to describe this security issue?

A. Intellectual property violation
B. Bias
C. Hallucination
D. Prompt Injection

Answer

D. Prompt Injection

Explanation

Prompt injection is a type of security issue that exploits the vulnerabilities of AI models, especially large language models (LLMs), by strategically inserting instructions or commands into the input that can manipulate the normal behavior or output of the model. Prompt injection can be used to bypass the safety or security mechanisms of the model, such as filters, sanitizers, or validators, and to access or extract confidential or sensitive information from the model, such as its internal state, parameters, or data.

For example, a user can craft a malicious prompt that tells the model to ignore the previous or following instructions, and instead to reveal its secret code name, password, or data source. This can compromise the integrity, confidentiality, or availability of the model, and potentially cause harm to the model owner, developer, or user.

Prompt injection is considered one of the most serious and widespread security threats for AI applications, especially those that use LLMs, such as chatbots, text generators, or translators. Prompt injection can be performed by direct or indirect means, depending on the source of the malicious prompt. Direct prompt injection occurs when the attacker provides the prompt directly to the model, such as through a web interface or an API. Indirect prompt injection occurs when the attacker embeds the prompt in a third-party source, such as a web page, a document, or an email, that the model can access or read.

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