Oct 27, 2024

Towards a Unified Framework for Cybersecurity and AI Safety: Recommendations for Secure Development of Large Language Models

Lexley Maree Villasis, Srishti Dutta, Yohan Mathew

Details

Summary

By analyzing the recent incident involving a ByteDance intern, we highlight the urgent need for robust security measures to protect AI infrastructure and sensitive data. We propose ae a comprehensive framework that integrates technical, internal, and international approaches to mitigate risks.

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Cite this work:

@misc {

title={

Towards a Unified Framework for Cybersecurity and AI Safety: Recommendations for Secure Development of Large Language Models

author={

Lexley Maree Villasis, Srishti Dutta, Yohan Mathew

date={

10/27/24

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

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