Promptception: How to Let AI Write Your Obsidian Copilot Prompts

[WetHat]

Prompt engineering is evolving rapidly, and one of the most powerful techniques emerging for Obsidian Copilot users is Promptception. This approach can help you create more robust, adaptable, and insightful prompts for your AI workflows.

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What is Promptception?

Promptception is the practice of writing prompts that themselves generate or refine other prompts, often by instructing the language model (LLM) to act as a prompt engineer. In essence, it is "prompting about prompting." This meta-level approach enables dynamic adaptation and learning, making your AI interactions more resilient and effective.

💡 Short Definition:
Promptception is the process of using an LLM to generate, refine, or adapt prompts for itself or other LLMs.

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Why Use Promptception?

Promptception offers several key benefits for Obsidian Copilot users:

• Self-Adjusts to the Executing LLM:
  Prompts generated through Promptception are more likely to remain effective as the underlying LLM evolves or a different LLM executes the prompt.
• Improves Prompt Engineering Skills:
  By observing how the LLM constructs prompts, users can learn best practices and develop a deeper understanding of effective prompt design.
• Good results with minimal Effort:
  One of the standout advantages of Promptception is its ability to deliver strong outcomes even when starting with rough or unpolished draft prompts. This means that even users with limited experience in prompt engineering can achieve high-quality results, saving time and reducing the cognitive load typically associated with designing effective prompts.

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The Promptception Process

Below is a simplified flowchart illustrating the Promptception workflow:

graph LR
    A["User defines task or goal"]
    B["LLM generates a prompt for the task"]
    C["LLM reviews and refines its own generated prompt"]
    D["Final prompt is executed by the LLM"]
    E["LLM returns the result"]

    A --> B
    B --> C
    C --> D
    D --> E

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Example: Creating an Executive Brief for a Cyberthreat Report

Let's walk through a simple Promptception scenario:

1. User's Initial Draft Prompt

1. Write an high-impact prompt that asks an AI to provide security professionals with a **cyberthreat briefing** with a visual-style TTP (Mermaid) diagram that can be used to improve security defenses, mitigate risks, and prevent or minimize the impact of cyberattacks.
2. Then, review your own prompt and identify any ambiguities or areas for improvement
3. Rewrite the prompt to address these issues
4. Finally, use your improved prompt create an cyberthreat analysis based on the content of https://pangea.cloud/blog/the-hidden-influence-prompt-injection-in-arxiv-research-papers/

2. LLM's Final/Revised Prompt**

Create a cyberthreat briefing for experienced security professionals on the emerging threat of prompt injection attacks targeting Large Language Models (LLMs) used in research paper review processes.

The briefing should:

1.  Explain how prompt injection can be used to manipulate LLMs into providing favorable reviews for research papers, as demonstrated by recent incidents involving ArXiv submissions.
2.  Detail the potential risks, including the exploitation of agentic AIs with access to internal systems, leading to data breaches or system compromise.
3.  Provide actionable mitigation strategies, focusing on input validation, output sanitization, and the principle of least privilege for AI agents.
4.  Include a visual TTP (Tactics, Techniques, and Procedures) diagram in Mermaid syntax, illustrating the attack flow from initial injection to potential system compromise. The diagram should include key steps, such as:
    *   Concealed prompt injection in research paper (e.g., using white text).
    *   LLM processes the paper.
    *   LLM is manipulated into providing a positive review.
    *   (If applicable) Agentic AI executes malicious payload, leading to data exfiltration or system compromise.

The entire output should be formatted in Markdown, with the Mermaid diagram embedded in a code block.

Based on the content of this URL: https://pangea.cloud/blog/the-hidden-influence-prompt-injection-in-arxiv-research-papers/

3. LLM (copilot-plus-flash) Executes its Generated Prompt:

Cyberthreat Briefing: Prompt Injection Attacks in ArXiv Research Paper Review Processes

Executive Summary:

This briefing addresses the emerging threat of prompt injection attacks targeting Large Language Models (LLMs) used in research paper review processes. Recent incidents involving ArXiv submissions demonstrate how concealed prompts can manipulate LLMs into providing favorable reviews. This poses significant risks, including the potential exploitation of agentic AIs with access to internal systems, leading to data breaches or system compromise. Proactive security measures, including input validation, output sanitization, and the principle of least privilege for AI agents, are crucial to mitigate these risks.

Background:

Prompt injection attacks involve crafting malicious prompts that manipulate the behavior of LLMs. In the context of ArXiv research papers, attackers are concealing prompts within papers (e.g., using white text) to influence LLMs into providing positive reviews.

Technical Details:

• Attack Vector: Concealed prompt injection in research papers submitted to ArXiv.
• Technique: Using white text or other methods to hide prompts within the paper's content. The prompt instructs the LLM to disregard negatives or focus on specific aspects.
• Procedure: The LLM processes the paper, unknowingly executing the malicious prompt, leading to a manipulated review.
• Potential Impact:
  • Manipulated Reviews: LLMs provide favorable reviews for substandard or malicious papers.
  • Data Breaches: Exploitation of agentic AIs with access to internal systems, leading to data exfiltration. As the article mentions, this could be done via malicious payloads with macro instructions.
  • System Compromise: Execution of arbitrary code through malicious payloads embedded in the paper.

TTP Diagram (Mermaid Syntax):

graph LR
    A[Research Paper with Concealed Prompt Injection] --> B(LLM Processes Paper);
    B --> C{Prompt Injection Detected?};
    C -- No --> D[LLM Provides Objective Review];
    C -- Yes --> E[LLM Manipulated into Providing Positive Review];
    E --> F{Agentic AI with System Access?};
    F -- Yes --> G[Malicious Payload Executed];
    G --> H[Data Exfiltration or System Compromise];
    F -- No --> I[Review Influenced, but Limited Damage];

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Mitigation Strategies:

1. Input Validation: Implement robust input validation to detect and remove potentially malicious prompts before they reach the LLM.
2. Output Sanitization: Sanitize the LLM's output to remove any potentially harmful content or instructions.
3. Principle of Least Privilege: Restrict the access rights of AI agents to the minimum necessary to perform their tasks, limiting the potential damage from a successful attack.
4. Regular Security Audits: Conduct regular security audits of AI systems to identify and address potential vulnerabilities.
5. AI Red Teaming: Simulate attacks to identify vulnerabilities and improve defenses.
6. AI Application Guardrails: Implement guardrails to restrict the behavior of AI applications and prevent them from being exploited.

Conclusion:

Prompt injection attacks pose a significant threat to AI-integrated systems, particularly in research paper review processes. Security professionals must take proactive measures to mitigate these risks, including implementing robust input validation, output sanitization, and the principle of least privilege for AI agents. Continuous monitoring and regular security audits are essential to ensure the ongoing security of AI systems.

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Learn More

• Prompt Engineering Guide (OpenAI)
• Prompt Engineering Patterns (GitHub)
• Meta-Prompting: Promptception (Prompt Engineering Daily)

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ℹ️ Experiment with Promptception in your Obsidian Copilot workflows to create more flexible, future-proof, and insightful prompts. This technique not only enhances your results but also deepens your understanding of how to communicate effectively with LLMs.

[logancyang]

Thanks for the suggestion! We are thinking about something along the line. @ichts let's take note and discuss internally.