AI-assisted coding tools have transformed the way developers write software. With platforms like GitHub Copilot, Gemini, and ChatGPT, teams can create features faster, automate repetitive tasks, and deliver products in record time.

However, while AI can generate functional code, it does not always generate secure code.

A 2025 study by Schreiber & Tippe analysing 7,703 AI-generated code files found over 4,200 distinct vulnerabilities across 77 weakness types, with Python-based code showing vulnerability rates as high as 18%.

As AI becomes embedded in modern development workflows, the likelihood of introducing exploitable flaws into production environments continues to grow.

This article highlights the five most common security risks found in AI-generated software and how to address them before they reach production.

1. Hardcoded Secrets

The risk:
AI models often reproduce patterns they have seen during training. This includes embedding sensitive information such as API keys, passwords, or tokens directly into the source code.
Hardcoded secrets can easily end up in public repositories, exposing systems and user data. A 2024 analysis by Cyberhaven found that 11% of code snippets generated by AI tools contained secrets like API keys and passwords.

Example:

API_KEY = “sk_test_5f9a12…”

A single exposed key can allow attackers to access services or customer data.

How to fix it:

• Use environment variables or secret management tools such as AWS Secrets Manager or HashiCorp Vault.
• Run secret scanning on every commit.
• Tools such as ArmourZero AVM Secret Scanning automatically detect and block exposed credentials before they reach production.

2. Outdated or Vulnerable Dependencies

The risk:
AI models are trained on vast datasets of existing code, which means their knowledge is frozen at a point in time. They may suggest libraries that were popular and secure then, but have since been found to have new vulnerabilities. However, many of these may contain known vulnerabilities or unpatched exploits.

For example, relying on an old version of Log4j could reintroduce the Log4Shell vulnerability into your project.

How to fix it:

• Regularly audit dependencies with Software Composition Analysis (SCA) tools.
• Include automatic dependency checks in your CI/CD pipeline.
• Use platforms such as ArmourZero AVM to continuously monitor and alert teams to vulnerable packages.

3. Insecure Input Handling

The risk:
AI can generate code that works functionally but omits validation or sanitisation steps.
This can lead to vulnerabilities such as SQL injection, Cross-Site Scripting (XSS), or command injection, which remain some of the most exploited flaws today.

Example:

query = “SELECT * FROM users WHERE username = ‘” + username + “‘;”

If the username contains malicious code, attackers can manipulate the query and access sensitive data.

How to fix it:

• Always use parameterised queries  (which separate the SQL command from the user data) and validate all input for expected format and length.
• Integrate Static Application Security Testing (SAST) into your development pipeline to detect unsafe code patterns.
• ArmourZero AVM scanning identifies input handling issues in real time.

4. Weak or Missing Authentication and Authorisation

The risk:
AI-generated code may include simplified authentication logic or omit authorisation checks, especially in prototype scripts. This often happens because AI models are trained on public code, including many simple tutorials or proofs-of-concept that intentionally skip robust security checks for the sake of clarity. This can expose APIs or administrative panels to unauthorised users.

Example:
A generated API endpoint that verifies only a username without checking a valid token or session.

How to fix it:

• Enforce multi-layered authentication and role-based access control (RBAC).
• Review generated code for missing permission checks.
• Integrate automated testing for authentication paths within CI/CD validation workflows.

5. Poor Error and Exception Handling

The risk:
AI-generated code often lacks robust error handling. It may print stack traces, expose database queries, or return detailed error messages that reveal system structures to attackers.

Example:

except Exception as e:

    print(e)

This approach can disclose internal logic or database details to anyone monitoring output or logs.

How to fix it:

• Use custom error messages that hide implementation details.
• Log errors securely using centralised systems such as Datadog or Splunk.
• Include runtime security monitoring in the pipeline to detect information leakage or abnormal behaviour.

Securing the AI Development Pipeline

AI enables teams to code faster, but speed must be matched with proactive security.
Manual reviews or post-deployment testing alone are insufficient to keep up with automated coding practices.

Automated Vulnerability Management (AVM) provides continuous scanning, testing, and remediation within the development workflow.
With ArmourZero AVM, every line of code, whether written by a human or generated by AI, is scanned and verified for security before release.

ArmourZero AVM helps teams to:

• Detects secrets and vulnerabilities automatically.
• Monitor dependencies for emerging threats.
• Prioritise real risks through AI-driven analysis.
• Remediate faster with guided, actionable fixes.

Final Thoughts

AI continues to reshape the way software is developed, creating opportunities for innovation and efficiency. The key to adopting AI safely lies in balancing innovation with responsibility.
By integrating continuous security practices and using the right automated tools, development teams can harness the benefits of AI-generated code without introducing unnecessary risks.

To learn more about how automated security fits into AI-driven development, visit ArmourZero AVM and discover how it helps secure AI-generated software.