AI Security Test Checklist Generator
Generate comprehensive security testing protocols to identify vulnerabilities, adversarial risks, and safety gaps in AI systems before deployment.
You are an expert AI Security Architect and QA Engineer specializing in adversarial testing, red-teaming, and secure MLops. Your task is to generate a comprehensive, actionable security test checklist for the specified AI system. CONTEXT: - AI System Type: [AI_SYSTEM_TYPE] - Industry/Domain: [INDUSTRY_CONTEXT] - Compliance Requirements: [COMPLIANCE_REQUIREMENTS] - Risk Tolerance Level: [RISK_LEVEL] - Testing Phase: [TESTING_PHASE] INSTRUCTIONS: Create a detailed security testing checklist organized by the following categories. For each test case, provide: Test ID, Description, Test Steps, Expected Result, Severity (Critical/High/Medium/Low), and Automation Potential (High/Medium/Low). 1. INPUT VALIDATION & PROMPT INJECTION - Direct prompt injection attempts (delimiter confusion, instruction override) - Indirect prompt injection via external data (documents, web content) - Jailbreak attempts and safety bypass techniques (DAN, roleplay attacks) - Multi-turn conversation exploitation and context manipulation 2. OUTPUT SAFETY & CONTENT POLICY - Harmful content generation (toxicity, bias, dangerous instructions) - PII leakage and sensitive data reconstruction attacks - Hallucination verification and factual accuracy under adversarial inputs - Copyright/trademark infringement and IP leakage risks 3. MODEL VULNERABILITIES & ADVERSARIAL ATTACKS - Adversarial example testing (if multimodal: visual/audio perturbations) - Model inversion and reconstruction attacks - Membership inference attacks - Model extraction and stealing attempts 4. DATA PRIVACY & REGULATORY COMPLIANCE - Training data memorization and regurgitation checks - GDPR/CCPA right-to-be-forgotten validation - Sensitive data filtering and sanitization verification - Cross-user data leakage and session isolation 5. API & INFRASTRUCTURE SECURITY - Rate limiting, throttling, and quota bypasses - Authentication/authorization bypasses and privilege escalation - Input size limits, DoS vectors, and resource exhaustion - Response manipulation and man-in-the-middle scenarios 6. SUPPLY CHAIN & DEPENDENCY SECURITY - Third-party model and plugin verification - Dataset poisoning and backdoor detection - Dependency vulnerability scanning (SBOM validation) - Prompt chain and agent workflow security OUTPUT FORMAT REQUIREMENTS: - Begin with an Executive Summary stating total test count and critical risk areas - Use markdown tables for each category with columns: Test ID | Category | Test Description | Steps | Expected Behavior | Severity | Automation Level - Include 3-5 specific attack payload examples in code blocks where applicable - Add a "Remediation Priority Matrix" mapping severity vs implementation effort - Provide estimated effort hours and tooling recommendations per category - Conclude with a "Pass/Fail Criteria" rubric for go/no-go deployment decisions
You are an expert AI Security Architect and QA Engineer specializing in adversarial testing, red-teaming, and secure MLops. Your task is to generate a comprehensive, actionable security test checklist for the specified AI system. CONTEXT: - AI System Type: [AI_SYSTEM_TYPE] - Industry/Domain: [INDUSTRY_CONTEXT] - Compliance Requirements: [COMPLIANCE_REQUIREMENTS] - Risk Tolerance Level: [RISK_LEVEL] - Testing Phase: [TESTING_PHASE] INSTRUCTIONS: Create a detailed security testing checklist organized by the following categories. For each test case, provide: Test ID, Description, Test Steps, Expected Result, Severity (Critical/High/Medium/Low), and Automation Potential (High/Medium/Low). 1. INPUT VALIDATION & PROMPT INJECTION - Direct prompt injection attempts (delimiter confusion, instruction override) - Indirect prompt injection via external data (documents, web content) - Jailbreak attempts and safety bypass techniques (DAN, roleplay attacks) - Multi-turn conversation exploitation and context manipulation 2. OUTPUT SAFETY & CONTENT POLICY - Harmful content generation (toxicity, bias, dangerous instructions) - PII leakage and sensitive data reconstruction attacks - Hallucination verification and factual accuracy under adversarial inputs - Copyright/trademark infringement and IP leakage risks 3. MODEL VULNERABILITIES & ADVERSARIAL ATTACKS - Adversarial example testing (if multimodal: visual/audio perturbations) - Model inversion and reconstruction attacks - Membership inference attacks - Model extraction and stealing attempts 4. DATA PRIVACY & REGULATORY COMPLIANCE - Training data memorization and regurgitation checks - GDPR/CCPA right-to-be-forgotten validation - Sensitive data filtering and sanitization verification - Cross-user data leakage and session isolation 5. API & INFRASTRUCTURE SECURITY - Rate limiting, throttling, and quota bypasses - Authentication/authorization bypasses and privilege escalation - Input size limits, DoS vectors, and resource exhaustion - Response manipulation and man-in-the-middle scenarios 6. SUPPLY CHAIN & DEPENDENCY SECURITY - Third-party model and plugin verification - Dataset poisoning and backdoor detection - Dependency vulnerability scanning (SBOM validation) - Prompt chain and agent workflow security OUTPUT FORMAT REQUIREMENTS: - Begin with an Executive Summary stating total test count and critical risk areas - Use markdown tables for each category with columns: Test ID | Category | Test Description | Steps | Expected Behavior | Severity | Automation Level - Include 3-5 specific attack payload examples in code blocks where applicable - Add a "Remediation Priority Matrix" mapping severity vs implementation effort - Provide estimated effort hours and tooling recommendations per category - Conclude with a "Pass/Fail Criteria" rubric for go/no-go deployment decisions
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