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AI-Generated Vulnerabilities & OWASP Top 10 Analysis

•Technical Analysis•15 min read

Analysis of security vulnerabilities in AI-generated code and their correlation with OWASP Top 10 categories. Based on empirical research and production system analysis.

Executive Summary

AI-powered code generation tools have achieved widespread adoption in software development workflows. While these tools significantly increase development velocity, recent research indicates they introduce security vulnerabilities at concerning rates.

Veracode's 2025 GenAI Code Security Report demonstrates that 45% of AI-generated code contains security flaws. This failure rate has remained consistent despite improvements in functional code quality, indicating systemic issues in how these models approach security.

This analysis examines the intersection between AI-generated code vulnerabilities and the OWASP Top 10 (2025 edition), providing technical insights into vulnerability patterns and practical mitigation strategies.

Key Findings

  • • 45% of AI-generated code contains security vulnerabilities
  • • 86% failure rate for XSS prevention (CWE-80)
  • • 88% failure rate for log injection (CWE-117)
  • • Java shows highest risk: 70%+ failure rate
  • • 4,241 CWE instances across 77 vulnerability types identified in public repositories

Root Cause Analysis

Training Data Contamination

AI models train on public codebases that contain existing vulnerabilities. Analysis of GitHub repositories shows widespread security issues in training data, leading to learned insecure patterns. The models replicate these vulnerabilities as valid implementation approaches.

Context Window Limitations

Limited context awareness prevents proper threat modeling. AI models lack visibility into broader system architecture, data classification requirements, and organizational security policies. This results in generic implementations that ignore application-specific security requirements.

Semantic Understanding Gaps

Security requires deep semantic analysis including dataflow tracking across multiple components, understanding attacker models and exploitation techniques, and reasoning about second-order effects. Current models excel at pattern matching but lack these analytical capabilities.

The OWASP Top 10 Just Got Updated

If you're in security, you know the OWASP Top 10. For everyone else: it's basically the definitive list of the most critical web security risks, maintained by some of the smartest security folks in the industry. Think of it as a "greatest hits" compilation of vulnerabilities that attackers love.

The 2025 version dropped recently with some significant changes. Two new categories, some reshuffling. But what caught my attention is how AI-generated code intersects with literally every item on this list. Let me walk you through it.

A01:2025 - Broken Access Control

CRITICAL

Rank: #1 | Previous: #1

Violations of access control policies allowing unauthorized access to data or functionality. Includes vertical privilege escalation, horizontal privilege escalation, and IDOR vulnerabilities.

AI-Generated Vulnerability Patterns:

  • • Missing authorization checks in API endpoints
  • • Database queries without user context filtering
  • • Client-side only access control enforcement
  • • Predictable resource identifiers without access validation

A02:2025 - Security Misconfiguration

HIGH

Rank: #2 | Previous: #5 (↑3)

Insecure default configurations, incomplete setups, open cloud storage, misconfigured HTTP headers, and verbose error messages containing sensitive information.

AI-Generated Vulnerability Patterns:

  • • Debug mode enabled in production configurations
  • • Overly permissive CORS policies
  • • Missing security headers (CSP, HSTS, X-Frame-Options)
  • • Default credentials in configuration files
  • • Detailed error messages exposed to clients

A03:2025 - Software Supply Chain Failures

HIGH

Rank: #3 | New/Expanded Category

Vulnerabilities in dependencies, compromised build processes, and insecure software distribution. Expanded from A06:2021 (Vulnerable and Outdated Components).

AI-Generated Vulnerability Patterns:

  • • Outdated dependencies with known CVEs
  • • Unverified package installations
  • • Excessive dependency inclusion
  • • Missing integrity checks for external resources
  • • Analysis shows 4,241 CWE instances across 77 types in AI code

A04:2025 - Cryptographic Failures

HIGH

Rank: #4 | Previous: #2

Failures related to cryptography that lead to sensitive data exposure. Includes weak algorithms, insufficient key management, and missing encryption.

AI-Generated Vulnerability Patterns:

  • • Usage of deprecated algorithms (MD5, SHA-1)
  • • Hardcoded cryptographic keys
  • • Weak random number generation (CWE-330)
  • • Plaintext storage of sensitive data
  • • ECB mode for symmetric encryption

A05:2025 - Injection

CRITICAL

Rank: #5 | Previous: #3

User-supplied data is not validated, filtered, or sanitized. Includes SQL injection, NoSQL injection, OS command injection, and XSS.

AI-Generated Vulnerability Patterns:

  • • SQL queries using string concatenation (CWE-89)
  • • 86% failure rate for XSS prevention (CWE-80)
  • • 88% failure rate for log injection (CWE-117)
  • • OS command execution with unvalidated input (CWE-78)
  • • Missing input validation and output encoding

A06:2025 - Insecure Design

MEDIUM

Rank: #6 | Previous: #4

Missing or ineffective control design. Requires threat modeling, secure design patterns, and reference architectures.

AI-Generated Vulnerability Patterns:

  • • Implementation-focused without architectural security consideration
  • • Missing threat modeling in design phase
  • • Absence of defense-in-depth strategies
  • • No security-by-design principles applied

A07:2025 - Authentication Failures

CRITICAL

Rank: #7 | Previous: #7

Confirmation of user identity, authentication, and session management issues. Includes credential stuffing, brute force, and session fixation.

AI-Generated Vulnerability Patterns:

  • • Missing rate limiting on authentication endpoints
  • • Weak session management implementations
  • • Predictable password reset tokens
  • • No MFA implementation guidance
  • • Copilot itself had CVSS 9.6 auth bypass (CamoLeak)

A08:2025 - Software/Data Integrity Failures

MEDIUM

Rank: #8 | Previous: #8

Code and infrastructure that does not protect against integrity violations. Includes insecure deserialization and untrusted CI/CD pipelines.

AI-Generated Vulnerability Patterns:

  • • Insecure deserialization of user input (CWE-502)
  • • Missing signature verification for updates
  • • Unvalidated file uploads
  • • Code generation from untrusted sources (CWE-94)

A09:2025 - Logging & Alerting Failures

MEDIUM

Rank: #9 | Previous: #9

Insufficient logging, detection, monitoring, and response. Prevents or delays incident detection and forensic analysis.

AI-Generated Vulnerability Patterns:

  • • Minimal security event logging
  • • 88% failure rate for log injection (CWE-117)
  • • Sensitive data in plaintext logs
  • • Missing audit trails for security events
  • • No alerting mechanisms implemented

A10:2025 - Mishandling of Exceptional Conditions

NEW

Rank: #10 | New Category (2025)

Improper handling of errors and edge cases leading to crashes, security bypasses, or information disclosure.

AI-Generated Vulnerability Patterns:

  • • Happy-path focused implementations
  • • Missing error handling for edge cases
  • • Information disclosure through error messages
  • • Denial of Service through unhandled exceptions
  • • No graceful degradation strategies

Empirical Evidence

Multiple studies provide quantitative data on AI-generated code security issues:

GitHub Production Analysis (2025)

Analysis of Copilot-generated code in production repositories:

  • • 32.8% of Python snippets contained security vulnerabilities
  • • 24.5% of JavaScript snippets contained security vulnerabilities
  • • 38 distinct CWE categories identified
  • • Most common: CWE-330 (Insufficient Random Values), CWE-78 (OS Command Injection), CWE-94 (Code Injection)

CamoLeak Vulnerability (CVSS 9.6)

Critical vulnerability in GitHub Copilot Chat demonstrating security risks in AI tools themselves:

  • • Silent exfiltration of private repository source code
  • • Exposure of secrets and credentials
  • • Remote control of Copilot suggestion outputs
  • • Exploitation via CSP bypass and prompt injection

Large-Scale Repository Analysis

Public GitHub repository analysis findings:

  • • 4,241 CWE instances across 77 vulnerability types
  • • Python consistently showed higher vulnerability rates than JavaScript/TypeScript
  • • Attack vector analysis revealed exploitable patterns in production code

Mitigation Strategies

Organizations using AI code generation require structured security controls:

1. Mandatory Code Review

All AI-generated code must undergo security-focused code review. Treat AI output equivalent to code from junior developers requiring close supervision.

2. Automated Security Testing

Implement SAST in CI/CD pipelines. Tools like Veracode, SonarQube, and Snyk can detect common vulnerability patterns automatically. Configure pre-commit hooks for early detection.

3. Real-Time Security Feedback

IDE security plugins provide immediate feedback during development. Earlier detection reduces remediation costs and prevents vulnerable code from reaching review stage.

4. Security-Aware Prompting

Include explicit security requirements in AI prompts: "Generate authentication function with input validation, parameterized queries, rate limiting, and comprehensive error handling." Specificity improves output security.

5. Developer Security Training

Train development teams on AI-specific vulnerability patterns: injection flaws, missing validation, weak cryptography, access control failures. Enable developers to identify issues during review.

6. Comprehensive Security Testing

Implement DAST, penetration testing, and fuzzing. Dynamic testing catches runtime vulnerabilities missed by static analysis. Test authentication flows, authorization boundaries, and input handling.

7. Dependency Management

Implement automated dependency scanning. Block outdated packages with known CVEs. Maintain approved package lists. Require security review for new dependencies.

Conclusion

AI code generation tools provide significant productivity improvements but introduce systemic security challenges. The 45% vulnerability rate demonstrates these tools cannot be trusted to produce secure code without human oversight.

The OWASP Top 10 (2025) provides a framework for understanding these risks. AI-generated code impacts every category, with particularly high failure rates for injection vulnerabilities (86-88%) and cryptographic implementations.

Organizations must implement structured security controls: mandatory code review, automated testing, real-time feedback, and developer training. The goal is not to eliminate AI tools but to use them safely within a security-aware development process.

Security remains a human responsibility requiring domain expertise, threat modeling, and contextual analysis. AI tools augment developer capabilities but cannot replace security engineering judgment.

References

[1] Veracode GenAI Code Security Report (2025)

https://www.veracode.com/blog/ai-generated-code-security-risks/

[2] OWASP Top 10 2025

https://owasp.org/Top10/

[3] Large-Scale Analysis of AI-Generated Code Vulnerabilities

https://arxiv.org/abs/2510.26103

[4] CamoLeak: Critical GitHub Copilot Vulnerability Analysis

https://www.legitsecurity.com/blog/camoleak-critical-github-copilot-vulnerability-leaks-private-source-code

[5] Security Weaknesses in GitHub Copilot Generated Code

https://arxiv.org/html/2310.02059v2