Adversarial Robustness and LLM Red Teaming: A Unified Review of Security Toolkits

• Author(s): S. Muthuvel; Akaassh Sundar
• Paper ID: 1711918
• Page: 678-681
• Published Date: 12-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

Abstract

As advanced machine learning (ML) and large language model (LLM) systems are deployed at scale, the security perimeter has expanded to include both classical adversarial ML threats and LLM-specific risks such as prompt injection, jailbreaks, and sensitive information leakage. This paper presents a structured comparison of open source and community toolkits spanning these domains, covering canonical robustness libraries and orchestration utilities for deployment alongside modern LLM and agent security tooling for attack automation, red teaming, and runtime defenses, plus adjacent capabilities in deception, reverse engineering, and data centric audit/visualization.

Keywords

Adversarial robustness, AI security, red teaming, large language models (LLMs), jailbreaks, prompt injection, guardrails, Responsible AI governance, CI/CD integration

Citations

IRE Journals:
S. Muthuvel, Akaassh Sundar "Adversarial Robustness and LLM Red Teaming: A Unified Review of Security Toolkits" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 678-681 https://doi.org/10.64388/IREV9I5-1711918

IEEE:
S. Muthuvel, Akaassh Sundar "Adversarial Robustness and LLM Red Teaming: A Unified Review of Security Toolkits" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1711918