AI-Generated Code Risk Analysis
Comprehensive risk assessment of AI-generated code organized by threat category. Understand the likelihood and impact of security risks when using AI coding assistants like Copilot and Cursor.
Risk-Based Security Approach
Not all AI-generated code risks are equal. Understanding both likelihood and impact helps prioritize security efforts. Critical risks with high likelihood require immediate attention before deployment.
Authentication & Access Control Risks
Weak Authentication
Plain text passwords, weak hashing algorithms, or missing authentication checks
Authorization Bypass
Missing permission checks allowing horizontal or vertical privilege escalation
Session Management Flaws
Predictable session tokens, no expiration, or insecure storage
Hardcoded Credentials
API keys, passwords, or tokens embedded directly in source code
Injection Attack Risks
SQL Injection
Unparameterized database queries vulnerable to SQL injection attacks
Command Injection
Shell commands constructed with unsanitized user input
XSS Vulnerabilities
User input rendered without proper escaping or sanitization
NoSQL Injection
MongoDB or other NoSQL queries vulnerable to operator injection
Data Exposure Risks
Sensitive Data in Logs
Passwords, tokens, or PII written to application logs
API Over-exposure
API responses include unnecessary sensitive fields or internal data
Client-Side Secrets
API keys or credentials exposed in frontend JavaScript bundle
Debug Information Leak
Stack traces, file paths, or system details exposed in errors
Cryptography Risks
Weak Hashing
Using MD5, SHA-1, or plain hashing for passwords instead of bcrypt/argon2
Insecure Random Generation
Math.random() or similar used for security-critical operations
No Encryption at Rest
Sensitive data stored unencrypted in databases or file systems
Weak TLS Configuration
Outdated TLS versions or weak cipher suites enabled
Business Logic Risks
Race Conditions
Concurrent operations without proper locking allow double-spend or duplication
Missing Rate Limiting
No throttling on authentication or resource-intensive endpoints
Payment Logic Flaws
Price manipulation, discount abuse, or payment bypass vulnerabilities
Workflow Bypass
Multi-step processes that can be skipped or executed out of order
Supply Chain & Dependency Risks
Vulnerable Dependencies
AI suggests outdated packages with known CVEs
Malicious Packages
AI hallucinates non-existent packages or suggests typosquatted versions
Excessive Permissions
Dependencies with more permissions than necessary
Unmaintained Libraries
Deprecated or abandoned packages with no security updates
Risk Mitigation Strategies
Automated Security Scanning
CriticalRun SAST, DAST, and dependency scanners on all AI-generated code
Mandatory Code Review
CriticalAll AI-generated security-sensitive code requires expert review before merge
Security-Focused Prompts
HighExplicitly request secure implementations in every AI prompt
Pre-deployment Testing
HighManual penetration testing before deploying AI-generated features
Related Resources
Assess Your Risk Exposure
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