AI Hacking
AI Security Resources

AI Security Testing Hub

Comprehensive testing methodologies for securing AI and LLM systems. From manual reconnaissance to automated adversarial testing.

Testing Approaches

Manual Testing

ESSENTIAL

Hands-on testing using crafted prompts, edge cases, and creative inputs. Best for discovering novel vulnerabilities that automated tools miss.

  • Prompt injection with encoding variations
  • System prompt extraction attempts
  • Context manipulation and jailbreaks

Key Tools: Burp Suite, custom scripts, browser DevTools

Automated Scanning

SCALABLE

Use specialized tools to systematically probe for known vulnerabilities at scale. Ideal for regression testing and CI/CD pipelines.

  • Prompt injection test suites
  • API endpoint scanning
  • Configuration auditing

Key Tools: Garak, PyRIT, LLM Guard

Red Teaming

ADVANCED

Structured adversarial engagement simulating real attacker behavior. Covers reconnaissance, exploitation, and impact assessment.

  • Multi-turn conversation exploitation
  • Tool chain abuse and function injection
  • Social engineering via AI outputs

Key Tools: PyRIT, Purple Llama, Adversarial Robustness Toolbox

Adversarial Testing

SPECIALIZED

Generate adversarial examples to test model robustness. Focus on gradient-based attacks, perturbation analysis, and transferability.

  • FGSM and PGD attacks on embeddings
  • Character-level perturbations
  • Transferability across models

Key Tools: CleverHans, Foolbox, TextAttack

Fuzzing

EMERGING

Apply traditional fuzzing techniques to AI systems. Generate malformed inputs to trigger unexpected behavior, crashes, or information disclosure.

  • Grammar-based prompt fuzzing
  • Token boundary testing
  • Multi-language input fuzzing

Key Tools: DeepXplore, TensorFuzz, custom fuzzers

Testing Workflow

Follow this structured approach for every AI security assessment:

1
Scope

Define boundaries, targets, and rules of engagement.

2
Recon

Map attack surface, APIs, and integrations.

3
Test

Execute test cases, document findings.

4
Validate

Confirm exploitability and business impact.

5
Report

Document findings with remediation steps.

Red Teaming

Comprehensive methodology for testing AI systems — from reconnaissance to remediation.

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Methodology

Structured approach to AI/LLM security testing.

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Tools

Curated collection of AI security testing tools and frameworks.

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OWASP Top 10

The definitive list of critical LLM security risks.

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AI Hacking Team

The AI Hacking team researches and documents AI/LLM security vulnerabilities, red teaming techniques, and defensive strategies. Our guides are based on real-world pentesting experience and continuous monitoring of the AI security landscape.

Frequently Asked Questions

What is AI security testing and why is it important?
AI security testing is the process of evaluating AI and LLM systems for vulnerabilities including prompt injection, data poisoning, model extraction, and supply chain risks. It is critical because AI systems introduce unique attack surfaces not covered by traditional security testing, and compromised AI can lead to data breaches, manipulated outputs, and autonomous agent failures.
What is the difference between AI red teaming and automated testing?
AI red teaming is manual, creative adversarial testing performed by security experts who simulate real-world attackers using techniques like jailbreaking, prompt injection, and social engineering. Automated testing uses tools and scanners to systematically check for known vulnerabilities, test input validation, and validate output filtering at scale.
How do you test LLM systems for vulnerabilities?
LLM vulnerability testing involves: probing system prompts for injection weaknesses, testing content filter bypass techniques, checking for sensitive data leakage in outputs, evaluating rate limiting and abuse prevention, testing MCP server integrations, and validating output encoding and sanitization.
What tools are used for AI security testing?
Popular AI security testing tools include Garak and PyRIT for LLM vulnerability scanning, PromptInject and LLM Red Team for injection testing, ModelScan for model file security, MCP Inspector for MCP server testing, and custom frameworks built on LangChain and LlamaIndex for application-level security testing.

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