AI-Generated Code Risk Analysis

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Outdated TLS versions or weak cipher suites enabled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AI suggests outdated packages with known CVEs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

Run SAST, DAST, and dependency scanners on all AI-generated code

Mandatory Code Review

All AI-generated security-sensitive code requires expert review before merge

Security-Focused Prompts

Explicitly request secure implementations in every AI prompt

Pre-deployment Testing

Manual penetration testing before deploying AI-generated features

AI Code Vulnerabilities

Complete taxonomy of AI code vulnerabilities

Code Quality Assessment

Quality vs security trade-offs analysis

Security Testing Tools

Tools for AI code security analysis

Broader Safety Implications

Safety risks beyond security vulnerabilities

Assess Your Risk Exposure

Identify which risks are present in your AI-generated codebase. VibeEval provides comprehensive risk analysis tailored to code from Copilot, Cursor, and other AI tools.