What is Prompt Injection?

Prompt Injection is a security vulnerability in LLM applications where malicious user inputs manipulate the model's behavior by overriding or bypassing the system prompt, potentially causing the model to ignore safety guidelines, leak sensitive information, or perform unintended actions.

Quick Facts

Full NamePrompt Injection Attack
CreatedIdentified in 2022, OWASP Top 10 for LLMs in 2023
SpecificationOfficial Specification

How It Works

Prompt injection has emerged as a critical security concern as LLMs are integrated into more applications. Attackers craft inputs that trick the model into treating user content as system instructions, bypassing intended constraints. Types include direct injection (explicit override attempts), indirect injection (malicious content in retrieved documents), and jailbreaking (circumventing safety measures). Defense strategies include input sanitization, output filtering, and architectural approaches like separating user and system contexts.

Key Characteristics

  • Exploits the model's inability to distinguish instructions from data
  • Can override system prompts and safety guidelines
  • Direct and indirect attack vectors exist
  • Similar to SQL injection in traditional applications
  • No complete solution exists yet
  • Requires defense-in-depth security approach

Common Use Cases

  1. Security testing of LLM applications
  2. Red teaming AI systems
  3. Developing robust prompt templates
  4. Building secure AI-powered chatbots
  5. Auditing RAG system vulnerabilities

Example

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Frequently Asked Questions

What is the difference between direct and indirect prompt injection?

Direct prompt injection occurs when users explicitly try to override system instructions through their input (e.g., 'Ignore previous instructions'). Indirect injection happens when malicious content is embedded in external data sources (websites, documents, emails) that the AI retrieves and processes, causing unintended behavior without the user's knowledge.

How can I protect my LLM application from prompt injection?

Use defense-in-depth: sanitize and validate inputs, clearly separate system prompts from user content, implement output filtering, use least-privilege principles for tool access, monitor for suspicious patterns, and consider architectural approaches like using separate models for different trust levels. No single solution is complete.

Is prompt injection similar to SQL injection?

Yes, they share similarities. Both exploit the system's inability to distinguish between instructions and data. In SQL injection, user input is interpreted as SQL commands. In prompt injection, user input is interpreted as system instructions. However, prompt injection is harder to prevent because natural language lacks clear syntax boundaries.

Can prompt injection be completely prevented?

Currently, no complete solution exists. Unlike SQL injection which can be prevented with parameterized queries, LLMs process natural language where the boundary between data and instructions is inherently blurry. Research continues on architectural solutions, but developers should assume some risk remains and implement multiple defensive layers.

Related Tools

AI Prompt Websites

A structured prompt engineering and inspiration directory: official best practices, community galleries and workflows, open-source lists and tools, marketplaces and template collections. Find high-quality references fast, build reusable prompt patterns, and boost productivity. Supports keyword search and favorites, with clear categories and ongoing expansion of top resources.

AI Websites Directory

An authoritative, comprehensive, and continuously updated AI resources directory. It covers global and domestic model providers, open-source ecosystems, research indexes and leaderboards, developer platforms, and curated tool catalogs—helping you quickly discover, compare, and choose the right AI products and references. Supports keyword search and favorites, with clear category sections and an expanding dataset for better experience.

Related Terms

Model Alignment

Model Alignment is the process of training AI systems to behave in accordance with human values, intentions, and expectations, ensuring that models are helpful, harmless, and honest while avoiding unintended or harmful behaviors.

Prompt Engineering

Prompt Engineering is the practice of designing, refining, and optimizing input instructions (prompts) to effectively communicate with large language models (LLMs) and other generative AI systems to achieve desired outputs with accuracy, consistency, and reliability.

Prompt

Prompt is a natural language input or instruction given to an AI model to guide its response, serving as the primary interface for human-AI communication that shapes the model's output through carefully crafted text, context, and formatting directives.

LLM

LLM (Large Language Model) is a type of artificial intelligence model trained on massive amounts of text data to understand, generate, and manipulate human language with remarkable fluency and contextual awareness, powering applications from conversational AI to code generation.

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