LLMs for Multi-Modal Knowledge Extraction and Analysis in
  Intelligence/Safety-Critical Applications
Brett Israelsen Soumalya Sarkar
Large Language Models have seen rapid progress in capability in recent years;
this progress has been accelerating and their capabilities, measured by various
benchmarks, are beginning to approach those of humans. There is a strong demand
to use such models in a wide variety of applications but, due to unresolved
vulnerabilities and limitations, great care needs to be used before applying
them to intelligence and safety-critical applications. This paper reviews
recent literature related to LLM assessment and vulnerabilities to synthesize
the current research landscape and to help understand what advances are most
critical to enable use of of these technologies in intelligence and
safety-critical applications. The vulnerabilities are broken down into ten
high-level categories and overlaid onto a high-level life cycle of an LLM. Some
general categories of mitigations are reviewed.