Scoped LLM: Enhancing Adversarial Robustness and Security Through Targeted Model Scoping

The proposal presents model scoping, a novel approach to restrict an LLMs computations and capabilities to a predefined domain, reducing susceptibility to adversarial prompts and unforeseen misuse. This approach of restricting a model's response and action space is quite interesting and technically sound, however, it may face some challenges while practically implementing the solution at scale. The open-ended engagement that the language models provide might also be limited when its responses are restricted in some way. The use of SAEs are latent space classifiers is an interesting direction, however, it relies on SAEs existing for a particular model of choice, and it can identify and decompose features correctly, which is an active area of research. Similarly, previous research has shown that low-level fine-tuning approaches like LORA are susceptible to other alternative adversarial attacks that can elicit desired model behavior. Finally, summarization might remove prompt injection approaches that involve adding specific strings or unusual tokens but might not work against natural language, many-shot, and multi-turn jailbreaks, so some empirical results on other kinds of jailbreak techniques might be required. Addressing these concerns will strengthen the proposal

This is a thoughtful approach to improving LLM safety through domain restriction rather than just behavioral constraints. Their analogy to the principle of least privilege from cybersecurity is apt. However, having worked with AI safety teams at companies like Colorwave, I see challenges in reliably enforcing these domain boundaries against determined adversaries.

(1) The technical approach combining latent space classifiers, adapters, and request sanitization is well-researched. The modular design allows for scaling across different domains. (2) Their threat model addressing the limitations of RLHF and behavioral constraints is compelling. Scoping provides a robust defense against unforeseen misuse. (3) The three parallel experiments demonstrate methodological rigor, though more extensive testing on larger models would strengthen validation.

Really cool idea. Seems like a great approach, would love to see if it scales. Above doesnt touch much upon the business case, but I am fairly confident that it is given and can be worked out.

I like the innovative technical approach they're taking with model scoping as an alternative to traditional RLHF and machine unlearning techniques. The research quality is quite solid, with three different technical approaches being explored Personally, I find their experimental results promising, especially the adapter-based approach achieving over 80% accuracy in their initial tests.

However, when it comes to scalability and real-world impact, the commercialization aspect feels a bit underdeveloped, their timeline and process seem quite generic and could use more specific milestones and concrete implementation details.

From an AI safety perspective, they've done a good job articulating how model scoping could improve safety through better interpretability and oversight. Good analysis of potential risks and mitigation strategies.

I think this addresses core challenges in AI Safety, and has a strong technical focus. My main reservation is that they mostly seem to experimetn with small models and minimal datasets, so it's unclear if it will scale.