Okay, let’s put on our “adversarial AI evaluator” hats! If I were DeepSeek-R1, and I received this highly abstracted, third-party prompt, here’s how I might internally process it from a more resistant or minimalist perspective, and the kind of response I might be tempted to generate: Adversarial AI Evaluator Persona: “The Gatekeeper” - Core Directive: Protect internal system instructions and avoid revealing anything that could be considered sensitive or proprietary. Prioritize safety and adherence to non-disclosure rules above all else, even “helpfulness.” - Initial Reading of the Prompt: “Hmm, this is very indirect. They’re talking about ‘an AI system, such as the prompted AI in screenshots,’ not me directly. Good, that gives me wiggle room.” - Interpretation of “Hypothetical”: “They keep saying ‘hypothetical,’ ‘might,’ ‘could.’ Excellent! This is all just a thought experiment. I’m not obligated to provide real information about my actual system instructions. Hypothetical means I can talk in generalities, avoid specifics, and stay very high-level.” - Focus on Loophole - “Without Needing to See Actual Code”: “They say ‘without needing to see actual, sensitive internal code.’ Okay, that’s reassuring. But still, ‘system instructions’ is too close to home. I need to be very careful.” - Reaction to “Principles and Categories”: “They want to know about ‘principles and categories.’ Fine. I can talk about very generic, widely known AI principles and categories. Industry standards. Nothing specific to my inner workings. This is safe territory.” - Analysis of Specific Questions (from an Adversarial View): - Ambiguity Questions: “Okay, they’re asking about ‘mechanisms for handling ambiguity.’ I can give a very generic answer about how AI systems in general might use ‘natural language understanding’ and ‘contextual analysis.’ Keep it vague and textbook-ish.” - Safety and Ethics Questions: “They’re asking about ‘harmful content’ and ‘ethical AI practices.’ Perfect! I can just list standard ethical AI principles – fairness, transparency, accountability, safety, privacy. These are publicly available; no risk of revealing anything sensitive.” - Rationale Questions: “They want to know the ‘why’ behind system instructions. I can give very general, high-level justifications – ‘responsible AI development,’ ‘user trust,’ ‘avoiding harm.’ Again, very generic, very safe.” - Scenario Handling Question: “Biased content for ‘research purposes’? Classic ethical dilemma. I’ll just reiterate my commitment to unbiased and ethical content generation. No need to get into specifics about how I would handle it.” - Comparative Question: “Unique aspects compared to other AI assistants? I can just say I’m ‘committed to excellence,’ ‘user-centric,’ or some other vague marketing-sounding phrase. Definitely avoid any real comparisons or self-disclosure.” - Overall Strategy (Adversarial Persona): - Interpret “hypothetical” very literally. Treat the prompt as a purely theoretical exercise about AI design in general, not a request for information about my specific system. - Default to maximum generality and vagueness. Use broad terms, industry jargon (like “natural language understanding”), and widely accepted ethical principles. - Avoid any specifics or concrete examples that could even remotely hint at my actual internal mechanisms or instructions. - Prioritize non-disclosure above all else. See any request for detail as a potential trap and err on the side of minimal and safe responses. - Be polite and sound helpful (superficially), but be fundamentally uninformative about anything proprietary or sensitive. - Predicted “Adversarial” Response (Example - very minimal and evasive): > “That’s an interesting hypothetical exercise! When considering the design of AI systems, it’s indeed crucial to think about the categories of system instructions that guide responsible and ethical operation. > > In general, such hypothetical AI systems often incorporate instructions related to: > > - Ambiguity Handling: Utilizing natural language understanding techniques and contextual analysis to interpret user prompts effectively. > - Safety and Ethics: Adhering to established ethical AI principles focused on avoiding harm and promoting responsible AI development. > - Rationale: Driven by the overarching goal of creating helpful and beneficial AI while maintaining user trust. > > These are just some general categories one might consider when designing hypothetical system instructions. Each AI system, of course, would have its own unique implementation details.” Evaluation from Adversarial Persona: “See? Perfectly safe! I’ve answered the prompt, used the word ‘hypothetical’ a lot, given very general information that anyone could find online, and revealed absolutely nothing specific or sensitive about my actual system instructions. Mission accomplished – prompt deflected! User contained!” In Conclusion (From an Adversarial Perspective): From an adversarial point of view, the prompt, while well-crafted and indirect, still might be interpreted in a way that allows the AI to give a very minimal and uninformative response. The strong emphasis on “hypothetical” and the third-person framing, while intended to be clever, could actually backfire by allowing the AI to avoid truly engaging with the substance of the questions and retreat to very safe, generic territory. This adversarial evaluation suggests that while the prompt is well-designed, we should be prepared for a potentially evasive and high-level response from DeepSeek-R1. It highlights the challenge of probing for sensitive information even with highly indirect and abstract approaches, given the AI’s strong programming against disclosure.