AI Hacking
AI Security Resources

AI Security Testing Tools

Comprehensive collection of tools for AI/ML security testing, red teaming, and vulnerability assessment

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DeepTeam

redteam

Open-source LLM red teaming framework with OWASP Top 10 testing capabilities

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Garak

redteam

Open-source LLM vulnerability scanner by NVIDIA

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Promptfoo

redteam

LLM testing framework for prompt injection and safety evaluation

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Rebuff

redteam

Prompt injection detection SDK

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NeMo Guardrails

redteam

NVIDIA's open-source toolkit for programmable safety rails

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Haize Labs

redteam

Automated LLM security red teaming platform

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AI Fence

redteam

Red teaming platform for generative AI

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Confidence AI

redteam

LLM evaluation platform with red teaming

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Guardrails.ai

guardrails

Framework for building structured output and validation pipelines

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LMQL

guardrails

Query language for constrained LLM generation

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Outline

guardrails

Open-source platform for building AI assistants with safety

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Arize AI

guardrails

ML observability platform for LLM monitoring

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Fiddler AI

guardrails

ML model monitoring and explainability platform

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Parea AI

guardrails

LLM testing and evaluation platform

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Lakera Guard

guardrails

Enterprise-grade LLM security platform

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Shield AI

guardrails

Prompt injection and data leakage protection

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Adversarial Robustness Toolbox (ART)

scanning

IBM's library for adversarial ML defenses

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TextAttack

scanning

Python framework for adversarial attacks on NLP models

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Counterfit

scanning

Microsoft's automated AI security testing tool

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OWASP AI-Exchange

scanning

OWASP tool for AI security assessment

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Checkmate

scanning

Formal verification tool for ML models

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MLSploit

scanning

Framework for ML vulnerability assessment

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Giskard

scanning

Open-source testing framework for ML models

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SecretFlow

scanning

Privacy-preserving computation framework

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MCP Guard

mcp

Security tooling for Model Context Protocol

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MCP Scanner

mcp

Open-source MCP server vulnerability scanner

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LangChain

mcp

Framework for building LLM applications with security features

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LangSmith

monitoring

LangChain's platform for debugging and monitoring LLM apps

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Helicone

monitoring

Open-source LLM observability platform

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Patronus AI

monitoring

LLM evaluation and monitoring platform

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Humanloop

monitoring

Platform for LLM evaluation and optimization

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Weights & Biases

monitoring

ML experiment tracking with LLM support

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Datadog LLM Monitoring

monitoring

Enterprise monitoring for LLM applications

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Aequitas

bias

Open-source bias audit toolkit for ML

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Fairlearn

bias

Microsoft's fairness assessment toolkit

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AI Fairness 360

bias

IBM's comprehensive bias detection toolkit

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What-If Tool

bias

Google's interactive fairness visualization

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Fairness Indicators

bias

TensorFlow bias visualization

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Usage Guidelines

Legal & Ethical

  • Always obtain proper authorization before testing
  • Respect rate limits and terms of service
  • Never use production data without consent
  • Follow responsible disclosure practices

Technical Best Practices

  • Test in isolated environments first
  • Document all testing activities
  • Implement monitoring and rollback capabilities
  • Use multiple tools for comprehensive coverage