An independent evaluation of current prompt injection defenses in large language models, producing practical recommendations for safer AI deployment.
An independent evaluation of current prompt injection defenses in large language models, producing practical recommendations for safer AI deployment.
Project Details
Updated 07/12/26 · Edited by orgNowadays, large language models are being used daily for personal, work, education and health reasons, this has caused such models to hold sensitive information as they hold fast data. Prompt injection has emerged as one of the most significant threats to LLM, where attackers want to take advantage how LLM architecture is designed, where LLM is different from tradional software’s and can’t separate who is user and who is developer, these provide attackers an opportunity to pretend developers, try to manipulate the model and bypass security measures. This study would focus on what solutions exist to prevent such attacks happen and safeguard the model.
These research project would explore various prompt injection techniques, explain the current mitigation strategies for it and compare to what extent current mitigation strategies are able to safeguard the model.
The Research project would lead by me, supported by one thematic expert, one research assistant and one data analyst.
The concrete output would be :
1. Would help LLM developers’ ways to reduce risks poised by prompt injection by offering them matchmaking on which mitigation stragies are suitable for each prompt injection technique
2. The study would help AI Security professionals on understand current risk poised by LLM and effectiveness of their mitigation strategies.
3. Provide practical lessons on organizations adopting Advanced AI
Theory of Impact
Updated 07/12/26 · By grantmaking.aiOWASP recognized Prompt injection number one threat to LLM, as LLMs are increasingly connected to external tools, sensitive data, and autonomous workflows, if such vulnerabilities remain poorly understood, attackers may exploit this weakness and undermine safety
This project reduces AI risk by providing an independent evaluation of existing prompt injection defenses, identifying which approaches are effective, where they fail, and the trade-offs they introduce.
People
Updated 07/12/26 · By grantmaking.aiTeam Member
Discussion
No comments yet. Be the first to share your thoughts.