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

arXiv: https://arxiv.org/abs/2406.11851 · HTML: https://mlsanity.com/paper/2406.11851/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [A Survey on Human-Centric LLMs](https://mlsanity.com/paper/2411.14491/) (2024)
- [Building a Domain-specific Guardrail Model in Production](https://mlsanity.com/paper/2408.01452/) (2024)

## Abstract (arXiv)

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.