A Systematic Review of Vulnerability Scanning Tools for Large Language Models (LLMs)

Karthikeyan Thirumalaisamy

Abstract: The increasing use of large language models (LLMs) in various contexts including; application environments, enterprise platforms, autonomous workflow automation has greatly increased the need for tools that will be able to provide an accurate assessment of the vulnerabilities associated with LLMs interaction based or prompt driven attack vectors. The past few years have seen numerous tools emerge that are designed specifically to scan LLM for vulnerabilities such as prompt injection weakness, jailbreaking exposure, misusing commands, generating unsafe responses and/or exposing information that should remain hidden. These tools however, differ in their approach, assumed methodologies, and validation practices, and currently there exists no single consensus on the relative merits of these tools or where each tool falls short. This paper will include a focused and comprehensive literature review of existing LLM vulnerability scanning tools, which will evaluate their methodology, capabilities, testing models, the range of vulnerability types covered, and the limitations of each. Through systematic analysis of the current state of LLM vulnerability scanners, this paper will identify what LLM vulnerability scanners can accurately identify now, and highlight the remaining gaps, and suggest directions for future scanner development that will allow for improved and more automated evaluation of LLM system security.

Keywords: Large Language Models (LLMs), Vulnerability Scanning, Prompt Injection Detection, Jailbreak Mitigation, Model Security Evaluation

How to Cite?: Karthikeyan Thirumalaisamy, "A Systematic Review of Vulnerability Scanning Tools for Large Language Models (LLMs)", Volume 14 Issue 12, December 2025, International Journal of Science and Research (IJSR), Pages: 543-548, https://www.ijsr.net/getabstract.php?paperid=SR251207022056, DOI: https://dx.doi.org/10.21275/SR251207022056