Selecting the appropriate tool for an AI red teaming engagement depends heavily on the target system, the engagement’s objectives, and your team’s expertise. The open-source landscape is vast and dynamic. These tables are designed to provide a comparative snapshot of prominent tools, helping you quickly identify candidates for specific tasks.
Use these comparisons not as a definitive ranking, but as a map to navigate the ecosystem. The “best” tool is the one that most effectively fits your unique context, from the model architecture you’re testing to the specific threat vectors you’re investigating.
General Adversarial Attack & Defense Frameworks
These are comprehensive libraries that offer a wide range of attack and defense implementations across different data modalities. They are often the starting point for most red teaming activities.
| Tool | Primary Focus | Supported Frameworks | Key Features |
|---|---|---|---|
| Adversarial Robustness Toolbox (ART) | Broad-spectrum attacks (evasion, poisoning, extraction, inference) and defenses. | TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM, CatBoost, MXNet | Extensive library of classic and modern attacks. Includes defenses and robustness metrics. Supports multiple data types (image, tabular, audio, video). |
| CleverHans | Education and research on adversarial attacks. A foundational library. | TensorFlow, PyTorch, JAX | Reference implementations of seminal attacks (e.g., FGSM, PGD). Excellent for learning the fundamentals. Less focused on production-grade tooling. |
| Foolbox | Benchmarking adversarial attacks and model robustness. | PyTorch, TensorFlow, JAX | Provides a unified interface to run a multitude of attacks against models. Focuses on producing minimal perturbations. Great for comparative analysis. |
| Counterfit | Automation and management of security assessments for AI systems. | Generic framework; adapts to models via a target abstraction layer. | Command-line interface for managing targets, running attacks, and logging results. Aims to operationalize AI security testing. |
NLP & LLM-Specific Tooling
As Large Language Models (LLMs) have become prevalent, a specialized set of tools has emerged to address their unique vulnerabilities, such as prompt injection, jailbreaking, and data leakage.
| Tool | Primary Focus | Supported Models / APIs | Key Features |
|---|---|---|---|
| TextAttack | Adversarial attacks on NLP models. | Hugging Face Transformers, custom PyTorch/TensorFlow models. | Highly modular framework based on “recipes”. Combines search methods, goal functions, and transformations to create attacks. Includes data augmentation features. |
| Garak | LLM vulnerability scanning. | Hugging Face, OpenAI, Cohere, and other API-based models. | Uses a probe-based system to test for dozens of specific failure modes like prompt injection, data leakage, toxicity, and hallucinations. Generates detailed reports. |
| LLM Guard | A security toolkit for protecting LLM interactions (defensive). | Integrates with LLM pipelines (e.g., LangChain). | While primarily defensive, its scanners (for topics, PII, toxicity, prompt injection) are invaluable for red teamers to understand detection mechanisms and develop bypasses. |
| Vigil | LLM security scanner for prompt injection, jailbreaking, and risk analysis. | API-based models (OpenAI, etc.), local models. | Combines a vector database of known attack strings with heuristic and model-based scanners. Provides a risk score for prompts and responses. |
Model Inspection & Explainability Tools
Understanding *why* a model makes a certain decision is critical for uncovering hidden biases, logical flaws, and unexpected correlations that can be exploited. These tools help peer inside the “black box.”
| Tool | Explainability Method | Supported Frameworks | Use Case in Red Teaming |
|---|---|---|---|
| SHAP (SHapley Additive exPlanations) | Game theory-based feature attribution. Calculates the contribution of each feature to a prediction. | Scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM | Identifying features that have a disproportionate influence on model outputs, which may indicate data poisoning or be a target for evasion attacks. |
| LIME (Local Interpretable Model-agnostic Explanations) | Model-agnostic local explanations. Approximates any model with a simple, interpretable one in the local vicinity of a prediction. | Any model with a predict_proba function. |
Explaining individual failing predictions to understand the root cause. Useful for debugging why a specific adversarial example works. |
| Captum | Model interpretability for PyTorch. | PyTorch | Provides a wide array of attribution algorithms (e.g., Integrated Gradients, DeepLIFT, Grad-CAM). Helps pinpoint influential neurons or input regions for vision and NLP models. |