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

References

[1] Adversarial Robustness Toolbox (ART) – GitHub: https://github.com/Trusted-AI/adversarial-robustness-toolbox

[2] HiddenLayer – Unpacking the AI Adversarial Toolkit: https://hiddenlayer.com/innovation-hub/whats-in-the-box/

[3] Bishop Fox – BrokenHill (GCG jailbreak automation): https://bishopfox.com/blog/brokenhill-attack-tool-largelanguagemodels-llm

[4] Protecto – Best LLM Security Tools of 2025: https://www.protecto.ai/blog/best-llm-security-tools-safeguarding-large-language-models/

[5] CleverHans – GitHub: https://github.com/cleverhans-lab/cleverhans

[6] APXML – Adversarial ML Benchmarking Tools: https://apxml.com/courses/adversarial-machine-learning/chapter-6-evaluating-model-robustness/benchmarking-tools-frameworks

[7] Microsoft Security Blog – AI security risk assessment using Counterfit: https://www.microsoft.com/en-us/security/blog/2021/05/03/ai-security-risk-assessment-using-counterfit/

[8] ITPro – Microsoft launches Counterfit: https://www.itpro.com/technology/artificial-intelligence-ai/359409/microsoft-open-source-counterfit-to-stop-ai-hacks

[9] Galah (LLM Honeypot) – GitHub: https://github.com/0x4D31/galah

[10] Adel – Decoding Galah (YouTube): https://www.youtube.com/watch?v=XGsm4Qcc_Ag

[11] Confident AI – LLM Guardrails Guide: https://www.confident-ai.com/blog/llm-guardrails-the-ultimate-guide-to-safeguard-llm-systems

[12] Snyk Labs – Red Team Your LLM Agents: https://labs.snyk.io/resources/red-team-your-llm-agents-before-attackers-do/

[13] LinkedIn – Top 18 AI Red Teaming Tools: https://www.linkedin.com/posts/nidhal-shaikh_ai-technewsae-neweratech-activity-7363427060523945985-MxxG

[14] ART Docs – https://adversarial-robustness-toolbox.readthedocs.io

[15] CleverHans v2.1.0 – arXiv PDF: https://arxiv.org/pdf/1610.00768.pdf

[16] Dreadnode – Automation Advantage in AI Red Teaming: https://dreadnode.io/blog/the-automation-advantage-in-ai-red-teaming

[17] arXiv (2025) – Automation Advantage in AI Red Teaming: https://arxiv.org/html/2504.19855v1

[18] BurpGPT – Product site: https://burpgpt.app

[19] LinkedIn – BurpGPT Automated Vulnerability Detection: https://www.linkedin.com/pulse/burpgpt-chatgpt-powered-automated-vulnerability-detection-reddy

[20] Anthropic Docs – Mitigate jailbreaks and prompt injections: https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/mitigate-jailbreaks

[21] GhidrAssist – GitHub: https://github.com/jtang613/GhidrAssist

[22] YouTube – Build an AI Powered Reverse Engineering Lab with Ghidra: https://www.youtube.com/watch?v=WOsVlzEXxJk

[23] Marktechpost (2025) – Top 18 AI Red Teaming Tools: https://www.marktechpost.com/2025/08/17/what-is-ai-red-teaming-top-18-ai-red-teaming-tools-2025/

[24] Stanford Hazy Research – Meerkat blog: https://hazyresearch.stanford.edu/blog/2023-03-01-meerkat

[25] arXiv (2025) – Bypassing LLM Guardrails: https://arxiv.org/abs/2504.11168I

@article{1711918,
      author = {S. Muthuvel, Akaassh Sundar},
      title = {Adversarial Robustness and LLM Red Teaming: A Unified Review of Security Toolkits},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {678-681},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711918.pdf},
      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},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1711918}
  }