Survey of Public Red-Teaming Frameworks for LLM: Techniques, Coverage, and Gaps

Karthikeyan Thirumalaisamy

Abstract: With LLMs (Large Language Model) being deployed in an increasing number of high-risk environments, red- teaming is becoming one of the most important methods to expose the potential for unsafe behavior, jailbreaking, and adversarial vulnerability prior to actual exposure via a real-world attack. Recently, a large number of publicly accessible, research-based, and open-source tools have been developed to help automate, or otherwise enhance, the red team's process. Although these tools vary greatly in terms of how they approach the problem, what they cover, and their level of development, there does not exist a single source of information that outlines the current landscape of publicly accessible tools for Red Teaming LLMs. Therefore, this paper will provide a systematic analysis of the various frameworks used for red-teaming of LLMs by examining the methodologies of each framework, the various types of attack strategies employed by each framework, the levels of automation provided with each framework, and the objectives of each framework related to evaluating the safety of the framework. The paper will also provide insight into commonalities, advantages/disadvantages, and operational limitations of each framework and identify areas where red-teaming tools lack sufficient capability such as: performing long-horizon multi-turn attacks, exploiting agent/tool interactions, testing adversarial in multiple languages, and creating dynamic adaptive attack loops. The authors' ultimate goal with this paper is to assist researchers, developers, and users of systems utilizing LLMs to understand the current landscape of publicly accessible red-teaming tools for LLMs and to provide guidance on future directions for developing robust, scalable, and comprehensive adversarial test tools for LLMs.

Keywords: LLM Security, Red-Teaming, Adversarial Testing, Jailbreak Generation, Automated Attack Frameworks

How to Cite?: Karthikeyan Thirumalaisamy, "Survey of Public Red-Teaming Frameworks for LLM: Techniques, Coverage, and Gaps", Volume 14 Issue 12, December 2025, International Journal of Science and Research (IJSR), Pages: 1788-1793, https://www.ijsr.net/getabstract.php?paperid=SR251221060319, DOI: https://dx.doi.org/10.21275/SR251221060319