AI Startup System Prompts Leaked: A Deep Dive into How Leading Companies Structure Their AI Instructions

A significant security breach has recently come to light, revealing the system prompts of several prominent AI startups – including Cursor, Lovable, Manuai, and others. These prompts, often considered a company’s “secret sauce,” represent billions of dollars in intellectual property and are crucial for defining how these AI tools function. This article delves into the details of this breach and analyzes how these companies structure their AI instructions.

The Value of System Prompts

The stolen prompts offer a unique glimpse into the inner workings of these startups. They reveal how these companies guide large language models (LLMs) to perform specific tasks, define functions, and adhere to best practices. For many of these companies, the prompt itself is a core component of their competitive advantage.

Similar Technology, Different Differentiation

It’s important to understand that many of these AI startups aren’t building LLMs from scratch. Instead, they are often wrappers around existing models like Gemini and Claude, and heavily customize Visual Studio Code. This means that prompt engineering and customization are key differentiators. They essentially take existing technology and build functionality *on top* of it, using carefully crafted prompts to control the AI’s behavior.

Analyzing the Cursor Prompt: A Detailed Look

Analyzes the prompt of Cursor, highlighting its structure and key components. The prompt begins by assigning a role to the AI (“You are a powerful agent and coding assistant”), specifying the underlying model (Cloud 3.7 Sonnet – though the option to choose different models exists within the Cursor interface), and defining its operating environment (“You operate exclusively in Cursor, the best IDE”).

• Role Definition: The prompt assigns a specific role, instructing the AI to act as a coding assistant.
• Tool Definitions: The prompt features detailed instructions for using tools, including specific parameters and formatting requirements.
• XML Delimiters: The use of XML delimiters helps to structure the conversation and clearly identify user queries.
• Code Modification Instructions: The prompt includes precise instructions for modifying code, emphasizing the importance of generating code that can be immediately followed by the user.
• Formatting Requirements: A crucial aspect of the Cursor prompt is the requirement for precise formatting – including line numbers, filenames, and file content – for code changes. This allows for seamless integration within the Visual Studio Code environment.

Security Implications: Prompt Leaking and the Need for Robust Defenses

The prompts were stolen using a technique called “prompt leaking,” which involves crafting a specific prompt that tricks the AI into revealing its system prompt. This highlights the security risks involved in relying heavily on prompts and the need for robust defenses.

One potential defense is the use of “guard rail” prompts – specialized AIs that filter input and block potentially harmful or adversarial prompts. However, even with these defenses, the risk of prompt leakage remains.

The Competitive Landscape and the Future of AI

This breach underscores the importance of prompt engineering and customization in the competitive AI startup landscape. It demonstrates that the “secret sauce” for many AI companies isn’t necessarily the LLM they use, but *how* they instruct the LLM through carefully crafted system prompts.

As AI continues to evolve, the ability to effectively engineer and protect these prompts will be crucial for maintaining a competitive edge.

Repo link:

https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools/tree/main