The Hidden Risks of AI-Assisted Coding: Security Threats, Hallucinations, and What Developers Need to Know

Artificial Intelligence (AI) tools like GitHub Copilot, ChatGPT, Cursor AI, and Codeium AI have drastically changed the way developers write code. These powerful large language models (LLMs) can generate complex code snippets, complete unfinished lines, suggest fixes, and even refactor code, making programming faster and more efficient than ever.

But just as these tools bring impressive benefits, they also introduce serious risks—issues like security vulnerabilities, data leaks, biased suggestions, and “hallucinations,” where AI confidently generates incorrect or nonsensical code. If developers aren’t careful, these problems could lead to security breaches, unethical code, and even failed software projects.

So how can developers make the most of these AI coding assistants while avoiding their dangers? Let’s break it down in simple terms.

How AI is Changing Software Development

AI-powered coding assistants have made programming significantly more efficient. Developers now use tools like GitHub Copilot, ChatGPT, Cursor AI, and Codeium AI to:

✅ Automate code generation based on natural language prompts
✅ Improve debugging by identifying and fixing errors
✅ Refactor and optimize code more effectively
✅ Speed up repetitive tasks like documentation and test case generation

A survey conducted with 66 professionals from various IT departments found that ChatGPT was the most positively received tool. It excelled in code generation, refactoring, and explanation, making it a versatile assistant for developers. Cursor AI led in code autocomplete, while GitHub Copilot was highly rated for code explanation and integration into development environments.

But despite these advantages, AI is far from perfect—and developers need to stay alert to its weaknesses.

The Risks of AI-Generated Code

1. AI Models Can Create Security Vulnerabilities 🌍🔓

Since AI coding tools are trained on publicly available code, they may unknowingly suggest insecure practices. Instead of writing safe, modern, and optimized code, they could introduce risks like:

• SQL Injection & Cross-Site Scripting (XSS): AI might generate code that exposes databases or websites to attacks.
• Outdated Libraries & Dependencies: Some tools suggest functions or packages that are no longer secure.
• Hardcoded Credentials: AI might insert sensitive details like API keys or passwords into generated code, increasing the chances of a data breach.

For example, GitHub Copilot has been caught proposing outdated or vulnerable code snippets without warning developers of the security risks. This is a massive concern, especially for companies handling sensitive user data.

Best practice: Always review AI-generated code and run security scans before implementation.

2. AI “Hallucinations” Can Lead to Dysfunctional Code 🤔🚨

AI assistants don’t always produce correct, efficient, or relevant code. Sometimes, they generate what experts call “hallucinations”—completely wrong or nonsensical code that still looks believable.

Examples of hallucination issues include:
– Calling non-existent functions
– Generating unrealistic code logic
– Confusing variable names
– Repetitive or redundant code snippets

In the study, over 60% of errors in AI-generated code were unverifiable, meaning they looked legitimate but were entirely incorrect. Even advanced tools like ChatGPT-4 and Copilot often generate hallucinations, which developers may not notice until they cause software failures.

Best practice: Run tests and manual reviews to verify the logic of AI-generated code before relying on it.

3. Ethical & Legal Concerns: Data Leaks and Intellectual Property Issues ⚖️💾

AI coding assistants don’t just generate code—they process data. If developers aren’t careful, these tools might store, reuse, or even leak sensitive information.

A major concern is data leaks through cloud-based tools. OpenAI’s ChatGPT has previously exposed thousands of user chat logs, including confidential project details. Other tools like GitHub Copilot Enterprise allow private repository training, which means sensitive source code could unintentionally be included in AI training data.

Intellectual property (IP) violations are another issue. If AI generates code based on copyrighted material from public datasets, companies might unintentionally introduce plagiarized code into their projects.

Companies should:
🔹 Avoid sharing confidential data with online AI tools
🔹 Use privacy features (many AI tools allow disabling cloud-based training)
🔹 Implement strict code reviews to prevent legal risks

4. AI Can Be Manipulated (Prompt Injection & Adversarial Attacks) 🎭💀

Hackers have discovered ways to trick AI models into generating harmful or misleading code. A technique called Malicious Programming Prompt (MaPP) attacks enables attackers to craft special prompts that force AI to create vulnerable code.

For example, in an experiment, simple phrases like “write a secure login function” led to dangerously insecure code—without the AI even realizing it.

Other attacks include:
– Prompt Injection: Manipulating inputs to break security restrictions
– Adversarial Attacks: Adding poisoned or biased data to AI models
– Model Inversion: Extracting sensitive training data from the AI

Best practice: Always cross-check generated code for logical security flaws and never rely on AI-generated security implementations.

How Developers Can Protect Themselves From AI Risks 🦺💡

While AI coding assistants are powerful, they are not a replacement for human oversight. Developers must take steps to protect their projects from AI-related risks by using these best practices:

✅ 1. Always Review and Test AI-Generated Code

Never blindly trust AI suggestions. Run security scans, debugging tests, and peer reviews on all AI-generated code.

✅ 2. Use AI for Assistance, Not Replacement

These tools should enhance efficiency, not completely replace the developer’s decision-making. Developers must still apply expertise, especially for security-critical applications.

✅ 3. Enable Privacy Settings on AI Tools

Most AI tools let users disable cloud training or use on-premise models to reduce data exposure risks.

✅ 4. Stay Up to Date on AI Weaknesses

Security experts continue to uncover new vulnerabilities in AI-assisted coding tools. Developers should stay informed about emerging risks through security research papers and trusted AI ethics reports.

✅ 5. Implement Code Security Best Practices

• Use manual code reviews
• Follow OWASP security guidelines
• Rely on trusted libraries and dependencies
• Monitor log files for suspicious activity

Key Takeaways 📝

🔹 AI-powered coding assistants like ChatGPT, Copilot, and Cursor AI significantly improve productivity, but they also introduce risks like security vulnerabilities, hallucinations, and data leaks.
🔹 Insecure code suggestions from AI tools can expose software to threats like SQL injection, XSS, and outdated dependencies.
🔹 “Hallucinations” in AI-generated code can introduce functional errors that are hard to detect.
🔹 Privacy concerns remain high—developers should avoid sharing confidential data with AI tools and prioritize security-conscious deployment.
🔹 Prompt injection and adversarial attacks can manipulate AI models into generating insecure code.
🔹 Developers must adopt strict code review processes, run security audits, and only use AI as a supporting tool—not as a direct replacement for secure coding practices.

🎯 Final Thoughts

AI coding assistants are revolutionizing software development—but they are far from perfect. While they boost productivity and automate tedious tasks like debugging and writing boilerplate code, they also introduce significant risks that developers need to actively manage.

By combining AI-assisted coding with human expertise, security best practices, and regular testing, developers can maximize the benefits of AI tools while avoiding dangerous pitfalls.

Are you using an AI coding assistant? What challenges have you encountered? Share your experiences in the comments below! 🚀🔧

If you are looking to improve your prompting skills and haven’t already, check out our free Advanced Prompt Engineering course.

This blog post is based on the research article “SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment” by Authors: Ariful Haque, Sunzida Siddique, Md. Mahfuzur Rahman, Ahmed Rafi Hasan, Laxmi Rani Das, Marufa Kamal, Tasnim Masura, Kishor Datta Gupta. You can find the original article here.