GUARD-D-LLM: An LLM-Based Risk Assessment Engine for the Downstream uses of LLMs

• URL: http://arxiv.org/abs/2406.11851v1
• Date: Tue, 2 Apr 2024 05:25:17 GMT
• Title: GUARD-D-LLM: An LLM-Based Risk Assessment Engine for the Downstream uses of LLMs
• Authors: sundaraparipurnan Narayanan, Sandeep Vishwakarma,
• Abstract summary: This paper explores risks emanating from downstream uses of large language models (LLMs) We introduce a novel LLM-based risk assessment engine (GUARD-D-LLM) designed to pinpoint and rank threats relevant to specific use cases derived from text-based user inputs. Integrating thirty intelligent agents, this innovative approach identifies bespoke risks, gauges their severity, offers targeted suggestions for mitigation, and facilitates risk-aware development.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Amidst escalating concerns about the detriments inflicted by AI systems, risk management assumes paramount importance, notably for high-risk applications as demanded by the European Union AI Act. Guidelines provided by ISO and NIST aim to govern AI risk management; however, practical implementations remain scarce in scholarly works. Addressing this void, our research explores risks emanating from downstream uses of large language models (LLMs), synthesizing a taxonomy grounded in earlier research. Building upon this foundation, we introduce a novel LLM-based risk assessment engine (GUARD-D-LLM: Guided Understanding and Assessment for Risk Detection for Downstream use of LLMs) designed to pinpoint and rank threats relevant to specific use cases derived from text-based user inputs. Integrating thirty intelligent agents, this innovative approach identifies bespoke risks, gauges their severity, offers targeted suggestions for mitigation, and facilitates risk-aware development. The paper also documents the limitations of such an approach along with way forward suggestions to augment experts in such risk assessment thereby leveraging GUARD-D-LLM in identifying risks early on and enabling early mitigations. This paper and its associated code serve as a valuable resource for developers seeking to mitigate risks associated with LLM-based applications.

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