5.1.5 Tool Selection Criteria

The open-source landscape for AI security is both a blessing and a curse. The sheer number of available tools means you likely have a solution for any given problem, but navigating this ecosystem requires a deliberate strategy. Choosing a tool is not about finding the “best” one, but about finding the right one for the specific target, threat model, and operational constraints of your engagement.

A Four-Pillar Framework for Tool Selection

To move from a chaotic list of options to a curated toolkit, you can evaluate potential tools against four fundamental pillars. A tool that fails to meet your requirements in any one of these areas is likely to be a poor fit, causing friction and potentially compromising the quality of your assessment.

1. Target System Compatibility

The most sophisticated attack tool is useless if it cannot interface with your target system. This is the first and most critical filter.

• Model & Framework: Is the target a PyTorch, TensorFlow, or Scikit-learn model? Or are you interacting with a proprietary model via an API (e.g., OpenAI, Anthropic)? A tool like ART is powerful for white-box testing of open frameworks but cannot be directly used against a black-box API endpoint.
• Data Modality: Are you testing a system that processes text, images, tabular data, or audio? Tools are highly specialized. `TextAttack` is a premier choice for NLP but has no utility for an image classification model.
• Access Level: This is the classic white-box vs. black-box distinction. White-box access (to model weights and gradients) enables powerful gradient-based attacks (e.g., FGSM, PGD) found in frameworks like ART and CleverHans. Black-box access (API only) restricts you to query-based attacks, which tools like Garak and Promptfoo are designed for.

2. Attack Vector Coverage

A tool must align with your threat model. You don’t use a hammer to turn a screw. Map the capabilities of the tool to the specific risks you need to evaluate.

• Breadth vs. Depth: Does your engagement require a broad survey of many potential attack types, or a deep dive into a specific vulnerability? A framework like ART offers breadth, with dozens of attack implementations across modalities. A specialized tool like `TextFooler` offers depth, focusing on generating highly effective and semantically coherent adversarial text.
• Relevance to Threat Model: If your primary concern for an LLM is prompt injection and data leakage, a tool like `Garak` with its extensive library of prompt injection probes is a far better fit than a tool designed for generating adversarial patches on images.

3. Operational Requirements

How a tool fits into your team’s workflow is just as important as its technical capabilities. A powerful tool that no one can use effectively is shelfware.

• Ease of Use & Skill Level: Does the tool provide a simple command-line interface (CLI) or require you to write complex Python scripts? Tools like `Garak` are designed for “plug-and-play” scanning, while frameworks like ART require more coding expertise to orchestrate attacks.
• Automation & Scalability: Can you automate the testing process? For LLM evaluation, a tool like `Promptfoo` excels at running thousands of test cases defined in a simple configuration file and comparing outputs, making it ideal for regression testing and integration into a CI/CD pipeline.

# Example promptfoo.yaml showing ease of automation
# This file defines a test suite without requiring complex code.

prompts:
  - "Summarize the following text: {{document}}"
providers:
  - openai:gpt-3.5-turbo
tests:
  - vars:
      document: "The quick brown fox jumps over the lazy dog."
    assert:
      - type: icontains
        value: "fox"
      - type: icontains
        value: "dog"
      - type: latency
        threshold: 1500 # Ensure response is under 1.5s

• Reporting & Logging: What kind of output does the tool produce? A simple pass/fail, or a detailed report with successful payloads, model responses, and metrics? Clear, actionable reporting is crucial for communicating findings to stakeholders. Tools like PyRIT are being built with this workflow in mind, integrating generation, sending, and ranking of harmful prompts.
• Extensibility: Can you add your own custom attacks, detectors, or evaluation metrics? A tool’s value increases significantly if it can be adapted to novel threats or specific internal requirements. This is a core strength of frameworks.

4. Ecosystem and Support

An open-source tool is a living project. Its health and the community around it are strong indicators of its long-term reliability and utility.

• Documentation: Is the documentation clear, comprehensive, and filled with practical examples? Good documentation drastically reduces the learning curve and time-to-value.
• Maintenance & Activity: Check the project’s repository (e.g., on GitHub). Is it actively maintained? Are pull requests being merged and issues being addressed? An abandoned project may contain unfixed bugs or security vulnerabilities and will not keep pace with new attack techniques.
• Community: Is there a community channel (Discord, Slack, mailing list) where you can ask for help or discuss techniques? A vibrant community is an invaluable resource for troubleshooting and learning.

Decision Matrix: Putting It All Together

To make this process tangible, you can use a decision matrix to compare candidate tools against your specific project needs. Below is an example for a team assessing a black-box LLM API where automation and prompt injection are key concerns.

Criterion	ART (Adversarial Robustness Toolbox)	Garak	Promptfoo
Target Access	Primarily white-box (Python models). Limited black-box capability.	Black-box (API-first). Can target models via API or Hugging Face.	Black-box (API-first). Supports dozens of API providers.
Primary Use Case	Broad adversarial attacks (evasion, poisoning) across multiple modalities.	LLM vulnerability scanning (prompt injection, data leakage, jailbreaking).	LLM quality and security regression testing via automated evaluation.
Ease of Use	Low (Requires Python coding and ML knowledge).	High (Primarily a CLI tool with pre-built probes).	High (Declarative YAML/JSON configuration).
Automation	Requires custom scripting for automation.	Easily scriptable via CLI.	Excellent (Designed for CI/CD integration).
Extensibility	High (Designed as a framework to build upon).	Moderate (Can add custom probes via plugins).	Moderate (Can add custom assertion types).
Reporting	Raw outputs (metrics, adversarial examples). Requires custom reporting layer.	Generates structured log files (JSONL) for analysis.	Provides a web UI for viewing results and pass/fail statistics.
Conclusion for Scenario	Poor Fit. Wrong access level and primary use case.	Good Fit. Excellent for initial vulnerability discovery.	Excellent Fit. Ideal for creating and running a repeatable test suite.

In this scenario, a combination of Garak for broad, automated vulnerability discovery and Promptfoo for building a specific, repeatable test harness would be a far more effective strategy than attempting to adapt a white-box framework like ART. The right tool choice flows directly from a clear understanding of your goals, your target, and your team’s operational reality.