Designing and Evaluating a Prompt-Injection-Resilient LLM Agent Using a Dual-Model Authorization Layer

Keshav Khandelwal, Viraj Mishra and Nitish Reddy Molakatalla

Large Language Models (LLMs) are increasingly used as agents that do more than generate text. These agents can read external content such as webpages, emails, or documents, and they can also call tools, for example to search databases, access files, or send messages. A major security problem arises when untrusted content is mixed with instructions. This vulnerability is known as prompt injection.

Major: 
Computer Science
Exhibition Category: 
Engineering
Exhibition Format: 
Poster Presentation
Campus: 
University Park
Faculty Sponsor: 
Suman Saha
Poster Number: 
188

Award Winner

Engineering - Third Place