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OneShield -- the Next Generation of LLM Guardrails
Created by
Haebom
Author
Chad DeLuca, Anna Lisa Gentile, Shubhi Asthana, Bing Zhang, Pawan Chowdhary, Kellen Cheng, Basel Shbita, Pengyuan Li, Guang-Jie Ren, Sandeep Gopisetty
Outline
This paper proposes OneShield, a model-independent and customizable standalone solution to address the security, privacy, and ethical concerns arising from the rapid rise of large-scale language models (LLMs). OneShield aims to provide risk definitions, context-specific safety and compliance policy expression and declarations, and LLM risk mitigation capabilities tailored to each customer. This paper describes the framework implementation, scalability considerations, and OneShield usage statistics following initial deployment.
Takeaways, Limitations
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Takeaways:
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Providing practical solutions to safety and ethical issues in LLM
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Applicable to a variety of LLMs through a model-independent and customizable approach
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Mitigating risks through situational safety and compliance policies
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Validation of effectiveness by providing usage statistics after initial deployment
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Limitations:
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Further research is needed to determine the long-term effectiveness and safety of OneShield.
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There is a need to verify whether the continuously evolving characteristics of LLM can be fully covered.
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Difficulty in comprehensively managing and maintaining diverse risk factors and situation-specific policies.
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Further experiments and validation of scalability are needed.
