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

References

[3] . Its strength is throughput for offensive testing; its scope is intentionally narrow—focused on jailbreak payload engineering rather than comprehensive app security. 3.2.2. DreadnodecCrucible. Crucible is positioned as an open red teaming/CTF platform that curates challenges, tracks outcomes, and supports both manual and automated tactics [16, 17]. Organizations use it for skills development, tool bake offs, and community benchmarking of attack techniques. 3.2.3. BurpGPT. By bringing LLM analysis into traditional web security tooling, BurpGPT augments application assessments with generative reasoning about inputs, responses, and flow logic [4, 18, 19]. This bridges LLM risks (prompt injection, leakage) with existing AppSec processes—particularly relevant for AI powered web interfaces. 3.2.4. Guardrails. Guardrails style frameworks provide policy driven validation and filtering around LLM I/O, including prompt injection indicators, content controls, and schema/contract enforcement to keep applications within defined bounds [11, 20]. They are designed to be embedded in production pipelines and tuned to domain specific rules. 3.2.5. Snyk (LLM/AgentSecurity). Snyk Labs documents patterns for “red teaming your LLM agents” inside developer workflows, emphasizing CI/CD integration, automated tests for jailbreak susceptibility, and controls for supply chain style risks in agent tools and plug ins [12, 13]. This targets an increasingly important vector: LLM/agent behavior as code. 3.3. Deception, Reverse Engineering, and Data Visualization 3.3.1. Galah (LLM powered honeypot). Galah generates dynamic, believable responses via LLMs to entice and observe attacker behavior on web endpoints, with optional integrations to network sensors like Suricata [9, 10]. It is valuable for discovering novel tactics, techniques, and procedures (TTPs), though sophisticated adversaries may fingerprint LLM generated artifacts if deployments are not carefully tuned. Figure 2:- AI Security Toolkit Landscape 3.3.2. Ghidra with LLM plugins (e.g., GhidrAssist). Extensions for Ghidra apply LLMs to tasks such as summarizing functions, proposing deobfuscation hints, or creating training corpora from reverse engineering workflows—bringing generative assistance to a classically manual discipline [21, 22] 3.3.3. Meerkat. Meerkat emphasizes visualization first analysis using embeddings, clustering, and comparative views to audit datasets and detect fragility or distributional shifts that could amplify adversarial risk [23, 24]. It focuses on data diagnostics rather than attack generation, making it complementary to robustness toolkits. IV. DISCUSSION AND SYNTHESIS ART remains a comprehensive choice for robustness evaluation across modalities, while CleverHans continues to serve as a clean, research oriented baseline. Counterfit adds operational leverage by orchestrating attacks against deployed systems, facilitating realistic assessments that mirror production constraints [1, 2, 6, 7]. 4.1. LLM Threat Landscape The LLM stack has driven rapid specialization— automated jailbreakers (BrokenHill), red team challenge platforms (Crucible), and runtime defenses (Guardrails, Snyk) illustrate a shift toward toolchains that blend automated and human led tactics [3, 11–13, 16]. AppSec aligned utilities like BurpGPT fold LLM risks into established security lifecycles [4, 18]. 4.2. Convergence with Traditional Security. Honeypots (Galah), reverse engineering helpers for binaries (Ghidra plug ins), and data centric visualization (Meerkat) show how generative techniques are permeating adjacent security domains. The unifying trend is modular, API driven integration so these tools can fit CI/CD, SecOps, and governance controls without bespoke plumbing [9, 21, 22]. 4.3. Gaps & Research Needs Many LLM defenses are early stage; bypasses and transferability of attacks remain common, especially across languages, multi turn dialogues, and multimodal inputs [20, 25]. The field needs stronger shared benchmarks, reproducible datasets, and community exercises (e.g., CTFs) that reflect realistic threat models and deployment contexts [16, 17]. V. CONCLUSION Modern AI security requires dual coverage: proven adversarial ML methods for traditional models and specialized LLM/agent defenses for generative systems. In practice, teams pair ART/CleverHans (analysis & baselines) with Counterfit (orchestration) to test classical ML, then extend with BrokenHill/Crucible (offense), Guardrails/Snyk (defense & SDLC), and BurpGPT (AppSec integration) for LLM applications. Deception (Galah), RE assistance (Ghidra plug ins), and data visualization (Meerkat) round out blue team visibility. Organizations should embed these tools behind governance gates, automate them in pipelines, and contribute findings to shared evaluations that raise the bar for robust and trustworthy AI. 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=WOsVlzE XxJk

[23] Marktechpost (2025) – Top 18 AI Red Teaming Tools: https://www.marktechpost.com/2025/08/17/wha t-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}
  }