The Cybersecurity Risks of AI-Generated Code in Software Development

SHIKSHA ROY | DATE: APRIL 26, 2025

Artificial Intelligence (AI) is transforming software development, enabling faster coding, automation, and efficiency. However, AI-generated code also introduces new cybersecurity threats, particularly for businesses relying on automated programming tools. Without proper oversight, AI-written programs can contain hidden vulnerabilities, exposing organizations to malware protection failures, ransomware assessment gaps, and data breaches. In this blog, we’ll explore the risks of AI-generated code and how businesses—especially small and medium-sized enterprises (SMEs)—can mitigate them through cybersecurity protection best practices, vulnerability assessment in cyber security, and cyber security training.

The Hidden Dangers of AI-Generated Code

AI-powered coding assistants like GitHub Copilot and ChatGPT can accelerate development but may also produce insecure code. Some key risks include:

Insecure Code Generation

AI models, particularly large language models (LLMs), can generate code that lacks secure coding practices. This can lead to vulnerabilities such as SQL injection, cross-site scripting (XSS), and insecure file handling. For instance, a data protection company might find that AI-generated code mishandles sensitive data, leading to potential data breaches.

Adversarial Attacks 

AI systems are susceptible to adversarial attacks. Malicious actors can exploit these vulnerabilities to manipulate AI models, causing them to generate insecure code or disclose sensitive information. Malicious actors can manipulate AI models to produce insecure code or reveal sensitive information. This is a significant concern for managed service providers (MSPs) offering cyber security services, as they need to ensure their AI tools are robust against such attacks.

Lack of Contextual Understanding 

AI-generated code may not fully understand the context in which it is used, leading to inappropriate or insecure implementations. This can be particularly problematic for small businesses that rely on AI tools for software development without comprehensive cybersecurity training.

Feedback Loops

AI models trained on existing codebases can inadvertently learn and propagate insecure coding practices. This can create a feedback loop where vulnerabilities are perpetuated across multiple projects. Cybersecurity compliance companies must be vigilant in monitoring and updating their AI models to prevent such issues.

How to Mitigate AI-Related Cybersecurity Risks

Cybersecurity Training for Developers

Providing cybersecurity awareness training for employees, especially developers, can help them recognize and address potential security issues in AI-generated code. Small business cyber security training programs can be particularly beneficial in this regard.

Regular Code Reviews and Penetration Testing 

Conducting regular code reviews and penetration testing in cyber security can help identify and rectify vulnerabilities in AI-generated code. This is essential for maintaining cybersecurity protection and ensuring compliance with industry standards.

Implementing Secure Coding Practices 

Encouraging the use of secure coding practices and frameworks, such as the OWASP Secure Coding Guidelines, can mitigate the risks associated with AI-generated code. Managed service providers for small businesses should emphasize these practices to their clients.

Utilizing Advanced Security Tools

Employing advanced security tools, such as vulnerability assessment in cyber security and network security detection, can help identify and mitigate risks in AI-generated code. These tools can provide real-time insights and alerts, enabling proactive cybersecurity risk management.

Collaboration with Cybersecurity Experts 

Partnering with cyber risk consulting firms and cybersecurity experts can provide valuable insights and support in managing the risks associated with AI-generated code. These experts can offer tailored solutions and best practices for enhancing cybersecurity protection.

Continuous Monitoring and Updates 

Regularly updating AI models and continuously monitoring their outputs can help mitigate the risks of insecure code generation. This is crucial for maintaining the integrity and security of software applications.

Final Thoughts: Balancing AI Efficiency with Cybersecurity

While AI-generated code boosts productivity, it requires cybersecurity help to prevent risks. By partnering with a data protection company, conducting cyber security risk assessment methodology reviews, and investing in small business cyber security training, organizations can safely harness AI without compromising security.

For businesses seeking cyber security near me, working with top MSP companies or an IT consulting services near me provider ensures robust defenses against evolving cyber security threats for small businesses.

Is your business protected? Contact a cyber solutions company today for a security risk assessment template and cyber security advisory to safeguard your digital assets.

Citations

1. CSET. (2024, November 19). Cybersecurity Risks of AI-Generated Code | Center for Security and Emerging Technology. Center for Security and Emerging Technology. https://cset.georgetown.edu/publication/cybersecurity-risks-of-ai-generated-code/

2. Farrar, O. (2025, March 21). Understanding AI vulnerabilities. Harvard Magazine. https://www.harvardmagazine.com/2025/03/artificial-intelligence-vulnerabilities-harvard-yaron-singer

3. Coker, J. (2025, April 25). Popular LLMs found to produce vulnerable code by default. Infosecurity Magazine. https://www.infosecurity-magazine.com/news/llms-vulnerable-code-default/

4. Chojnowski, L. (2023, February 10). 10 cyber security risks in software development and how to mitigate them - DEVTALENTS. DEVTALENTS. https://devtalents.com/cyber-security-during-software-development/

Image Citations

1. Blažić, K. (2023, January 5). How to harden machine learning models against adversarial attacks. ReversingLabs. https://www.reversinglabs.com/blog/how-to-harden-ml-models-against-adversarial-attacks

2. securecoding.org. (2023, June 2). Secure code review and testing Solutions: Comprehensive guide. https://www.securecoding.org/secure-code-review-testing/

3. Bug Ninza. (2024, August 6). A warning for developers: The hidden risks of AI-Generated Code | Must Watch | ChatGPT | CoPilot [Video]. YouTube. https://www.youtube.com/watch?v=OaIVBpBYwtg