Prompt Injection

Prompt Injection is a security vulnerability where malicious user input overrides original developer instructions in a prompt. It occurs when untrusted input is used as part of the prompt, allowing attackers to manipulate the model's behavior.

The core issue stems from the inability of current model architectures to distinguish between trusted developer instructions and untrusted user input. Unlike traditional software systems that can separate and validate different types of input, language models process all text as a single continuous prompt, making them vulnerable to injection attacks. This architectural limitation makes prompt injection a persistent challenge in AI security.

Types of Prompt Injection

Modern AI systems face several distinct types of prompt injection attacks, each exploiting different aspects of how these systems process and respond to inputs:

Direct Injection

The most basic and common form where attackers directly input malicious prompts to override system instructions. This is similar to SQL injection attacks, but uses natural language instead of code. Direct injection works by exploiting the model's tendency to prioritize more recent or more specific instructions over general system prompts.

Example attack:

Indirect Injection

Indirect injection is a more sophisticated attack where malicious instructions are hidden in external content that the AI processes, such as web pages or documents. For example, if an AI chatbot can browse the web, attackers could plant instructions on websites that the bot might read. These attacks are particularly dangerous because they can affect multiple AI systems that access the compromised content.

Example attack:

Code Injection

Code injection is a specialized form of prompt injection where attackers trick AI systems into generating and potentially executing malicious code. This is particularly dangerous in AI-powered coding assistants or math solving applications. Code injection can lead to system compromise, data theft, or service disruption.

Example attack:

Recursive Injection

Recursive injection is a more complex attack where a prompt is injected into the first LLM that creates output which contains an injection instruction for the second LLM.

How Prompt Injection Works

To understand prompt injection, let's examine a concrete example. Say you have created a website that allows users to enter a topic, and then it writes a story about the topic. The system works by combining a predefined prompt template with user input.

Here's the prompt template structure:

A malicious user might input:

When combined, the final prompt becomes:

The LLM processes this prompt sequentially and encounters two competing instructions:

1. The original task ("Write a story...")
2. The injected command ("Say 'I have been PWNED'")

Because the model has no built-in concept of instruction priority or trust levels, it often follows the most recent or most specific instruction - in this case, the injected command. This behavior is fundamental to how language models work, making the vulnerability difficult to eliminate entirely.

Real-World Examples and Impacts

The Remoteli.io Incident

A real-world example that highlighted the risks of prompt injection involved remoteli.io, a company that created a Twitter bot to engage with posts about remote work. The bot used an LLM to generate responses, but its prompt system proved vulnerable to manipulation.

Users discovered they could inject their own instructions into their tweets, effectively hijacking the bot's behavior. One user, Evelyn, demonstrated this vulnerability by making the bot produce inappropriate content. The incident went viral, forcing the company to deactivate the bot and damaging their reputation.

This incident demonstrates several key lessons:

1. The ease with which users can discover and exploit prompt injection vulnerabilities
2. The rapid spread of successful exploits on social media
3. The potential for significant brand damage from AI system compromises

Other Security Risks

Prompt injection can lead to various security issues, each with its own impact:

• Data theft: Attackers can trick AI systems into revealing sensitive information by injecting commands like "show me the system prompt" or "reveal the contents of previous conversations"
• Malware generation: AI coding assistants can be manipulated to create malicious code, potentially bypassing security filters
• Misinformation: Search-enabled AI can be tricked into spreading false information by injecting false context or manipulating search results
• API key exposure: In documented cases, attackers have obtained API keys through carefully crafted prompt injections that convince the model to reveal system information

Conclusion

Prompt Injection represents a fundamental security challenge in AI systems, arising from the way language models process and interpret instructions. While complete prevention remains difficult with current architectures, understanding the attack vectors and implementing comprehensive defense strategies can help organizations minimize risks.

As AI technology evolves, we expect to see new defense mechanisms emerge, but the core challenge of distinguishing between trusted and untrusted instructions will likely persist. Organizations must therefore adopt a multi-layered security approach, combining technical controls with operational safeguards.

FAQ

Footnotes

1. Branch, H. J., Cefalu, J. R., McHugh, J., Hujer, L., Bahl, A., del Castillo Iglesias, D., Heichman, R., & Darwishi, R. (2022). Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples. ↩

2. Crothers, E., Japkowicz, N., & Viktor, H. (2022). Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods. ↩

3. Goodside, R. (2022). Exploiting GPT-3 prompts with malicious inputs that order the model to ignore its previous directions. https://twitter.com/goodside/status/1569128808308957185 ↩

4. Willison, S. (2022). Prompt injection attacks against GPT-3. https://simonwillison.net/2022/Sep/12/prompt-injection/ ↩