The Security Risks of AI-Generated Code: What Nonprofits Need to Watch For
AI coding tools let nonprofits build custom software faster than ever. But nearly half of all AI-generated code contains security vulnerabilities, and resource-constrained organizations face the highest risk. Here's what you need to know before your next vibe coding project.
The promise is compelling: a program director with no technical background can describe a client intake form in plain English, and within hours have a working web application. Vibe coding tools like Bolt, Lovable, and Cursor have made this genuinely possible, and many nonprofits are seizing the opportunity to build custom tools that would have previously required expensive developers and months of work.
But there is a serious problem hiding inside this revolution. Research consistently shows that AI coding assistants produce code with security vulnerabilities at alarming rates. According to the Center for Security and Emerging Technology, a significant portion of AI-generated code contains security flaws, and the risks are not evenly distributed across organizations. Larger, well-resourced organizations with dedicated security teams can catch and fix these problems. Nonprofits, which typically operate without dedicated IT security staff, are far more vulnerable.
This matters enormously for nonprofits because the stakes are particularly high. Your organization likely handles sensitive data: donor financial information, client records that may include health or legal history, employee personally identifiable information, and grant-related financial data. A security breach doesn't just cost money; it can destroy donor trust, violate legal obligations, harm vulnerable clients, and threaten your organization's survival. Understanding the specific risks that AI-generated code introduces is the first step toward using these powerful tools responsibly.
This article explores the primary security risks associated with AI-generated code, explains why nonprofits are particularly exposed, and provides practical guidance for using vibe coding tools without putting your organization or clients at risk. This builds on the broader discussion of what tools like Bolt, Lovable, and v0 can realistically build, focusing specifically on the security dimension that many enthusiastic adopters overlook.
Why AI-Generated Code Has Security Problems
To understand why AI coding tools produce vulnerable code, you need to understand how they work. Large language models like those powering Bolt, Cursor, Claude, and GitHub Copilot were trained on enormous datasets of code from the internet. That code included many examples of poor security practices, outdated patterns, and outright vulnerabilities. The AI learned from what exists, not from what's ideal.
When you ask an AI coding tool to build a login page or a contact form, it generates code based on patterns it has seen before. If the vast majority of login forms in its training data didn't implement rate limiting or properly hash passwords, the AI may produce similar code. It isn't intentionally cutting corners; it's reproducing patterns from its training data, many of which were themselves insecure.
There's also what security researchers call the "illusion of correctness." AI-generated code typically looks professional, follows syntax rules, and runs without obvious errors. This creates false confidence. A staff member without programming expertise sees working software and assumes it's safe. What they cannot see are the hidden flaws: the database query that could be exploited by an attacker, the API key accidentally embedded in the frontend code, or the file upload handler that accepts any file type without validation.
Insecure Code Generation
AI models trained on public code repositories reproduce common security mistakes, including weak authentication patterns, improper data validation, and hardcoded credentials.
Model Vulnerabilities
The AI models themselves can be manipulated through prompt injection, producing code that appears legitimate but contains deliberate backdoors or malicious logic.
Downstream Impacts
Vulnerabilities in AI-generated code create cascading risks: compromised systems can expose donor databases, client records, and financial accounts to attackers.
The Most Common Security Vulnerabilities in AI-Generated Code
Not all vulnerabilities are equal. Some are theoretical inconveniences; others can result in complete data breaches. Understanding which types of flaws appear most frequently in AI-generated code helps you prioritize your review efforts and communicate risks to your board and leadership.
SQL Injection and Data Manipulation
One of the most dangerous and common vulnerabilities in web applications
When an AI builds a database-connected application, it may generate code that directly inserts user input into database queries without proper sanitization. This creates SQL injection vulnerabilities, where an attacker can type specially crafted input that manipulates the database, potentially reading all donor records, deleting data, or even taking control of the database server.
For a nonprofit with a donor database connected to a custom web form, this could mean an attacker gaining access to thousands of donor records including payment information, addresses, and giving history. AI coding tools frequently miss this vulnerability because the generated code appears to work correctly during normal use; the flaw only becomes apparent when someone specifically tries to exploit it.
- Affects any feature that reads or writes to a database based on user input
- Login forms, search functions, and filter interfaces are high-risk areas
- Can be prevented with parameterized queries, which AI tools sometimes use inconsistently
Hardcoded Credentials and API Key Exposure
A surprisingly common mistake that can expose entire systems
When you tell an AI tool to connect your application to a third-party service, it sometimes generates code that includes API keys, database passwords, or authentication tokens directly in the code file. This is an especially dangerous practice when the code ends up in a GitHub repository or is deployed to a hosting platform where the credentials become accessible to anyone who views the code.
This is far more common than most people realize. Automated bots continuously scan public repositories for accidentally exposed credentials. Within minutes of a new repository becoming public, these bots can discover and exploit leaked API keys. For a nonprofit, this might mean an attacker gaining access to your email service and sending fraudulent donation solicitations, or accessing your payment processor and initiating fraudulent transactions.
- Credentials should always be stored in environment variables, never in code
- Review all generated code for any strings that look like passwords or tokens before deployment
- Use secret scanning tools in your code repository to catch these automatically
Broken Authentication and Access Control
When who can access what isn't properly enforced
Authentication (verifying who someone is) and authorization (determining what they can do) are among the most complex areas of application security, and areas where AI tools frequently generate flawed code. A common pattern in AI-generated applications is client-side access control, where the user interface hides certain features from regular users, but the underlying data is still accessible via direct API calls.
For a nonprofit building a case management system with different access levels for case workers and supervisors, this could mean that case workers can access supervisory-level reports simply by knowing the right URL or API endpoint. The AI built visible restrictions in the interface but didn't enforce them in the application logic that actually retrieves data. This type of flaw is particularly difficult to detect without deliberate security testing.
- Always verify access control on the server side, not just the user interface
- Test access control by attempting to access restricted areas with different user accounts
- Multi-user applications with sensitive data require the most rigorous access control review
Dependency and Supply Chain Risks
Hidden vulnerabilities in the packages your code uses
When an AI generates code for a web application, it typically imports dozens or even hundreds of third-party packages and libraries. Each of these dependencies is a potential security risk. Security researchers have found that a significant portion of AI-suggested package dependencies either don't exist or have known vulnerabilities. Attackers have begun registering packages with names similar to popular legitimate packages, hoping that AI tools will suggest them.
Even when the packages themselves are legitimate, they may contain known security vulnerabilities that haven't been patched in the version the AI suggested. Because the dependency chain can include hundreds of packages, many of which you've never heard of, auditing all of them manually is impractical without automated tools. A single vulnerable dependency can compromise an otherwise secure application.
- Run automated dependency scanning before deploying any AI-generated application
- Verify package names carefully, especially less well-known ones the AI suggests
- Set up automated dependency updates and vulnerability alerts after deployment
Why Nonprofits Face Disproportionate Risk
Security researchers who study AI-generated code vulnerabilities have noted that the risks are not evenly distributed across organizations. Larger, well-resourced companies with dedicated security engineers can afford to review AI-generated code carefully, run automated security scans, and conduct penetration testing before deployment. Nonprofits typically cannot.
The very appeal of vibe coding tools for nonprofits, which is that they allow non-technical staff to build software quickly and cheaply, creates the conditions for security gaps. A program manager who builds a client intake system in an afternoon has neither the expertise nor the time to audit the generated code for SQL injection vulnerabilities. They see working software and consider the project complete.
No Dedicated Security Staff
Most nonprofits don't have a Chief Information Security Officer or even a dedicated IT staff member. Security reviews of AI-generated code require expertise that simply doesn't exist in most organizations. This means vulnerabilities that a professional security engineer would catch in minutes can persist indefinitely in production systems.
High-Value, High-Stakes Data
Nonprofits often hold exactly the kind of data attackers want: donor payment information, client health and legal records, social security numbers for benefits clients, and financial account information. A breach of this data carries legal liability, regulatory consequences, and severe reputational damage that can threaten organizational survival.
Assumed Trust from Mission
Nonprofits often benefit from elevated trust from donors, clients, and community members who assume that mission-driven organizations handle their data carefully. This trust can create complacency about security practices. It can also mean that when breaches do occur, the damage to trust and relationships is particularly severe.
No Security Testing Budget
Professional penetration testing, security audits, and automated security scanning tools all cost money. Nonprofits that have invested in building a free or low-cost custom application using AI tools often lack budget for security testing. The cost savings from vibe coding tools can be erased many times over by a single breach.
What to Build (and What Not to Build) with AI Coding Tools
The security risks of AI-generated code don't mean these tools have no place in nonprofit technology. They mean you need to be thoughtful about which use cases are appropriate and which carry unacceptable risk. The key variable is sensitivity: how harmful would it be if this application were compromised?
Low-sensitivity internal tools, where the data is non-confidential and limited to staff, are generally reasonable candidates for AI-generated code with appropriate review. High-sensitivity applications that handle client personal information, financial data, or health records require either professional development with security review, or established commercial platforms with proven security track records. There is no amount of careful prompting that makes AI-generated code appropriate for a system storing social security numbers without professional security validation.
Lower Risk: Generally Appropriate
- Internal dashboards displaying non-sensitive aggregate data
- Staff scheduling tools with only internal email addresses
- Volunteer shift sign-up pages with minimal personal data
- Event registration forms for public events
- Resource libraries and document repositories for staff
- Meeting note templates and automated report generators
Higher Risk: Use Established Platforms Instead
- Client intake systems collecting health, legal, or financial information
- Any system that processes or stores payment card information
- Case management systems with client identifiers and case notes
- Systems collecting Social Security Numbers or government ID numbers
- Applications with role-based access to sensitive donor data
- HIPAA-covered health information in any form
This framework isn't about avoiding AI coding tools entirely; it's about right-sizing their use to your security capacity. If you genuinely need a client intake system, platforms like Salesforce Nonprofit Success Pack, Social Solutions Apricot, or even Microsoft Power Apps with appropriate security configuration are more appropriate than AI-generated custom code. The upfront cost is higher, but so is the security baseline.
Practical Security Practices for AI-Generated Code
If you've determined that your use case is appropriate for AI-generated code, there are specific practices that meaningfully reduce your risk. These aren't theoretical best practices from security textbooks; they're concrete steps that any nonprofit can take without specialized security expertise.
Include Security Requirements in Your Prompts
The single most effective thing you can do is explicitly ask AI coding tools to prioritize security. Rather than simply describing what you want to build, add security requirements to your prompts. Something like "Build this contact form following OWASP security guidelines, using parameterized queries for database interactions, validating and sanitizing all user input, and storing any API keys in environment variables rather than in the code" produces significantly more secure output than describing only the functionality.
You can also ask the AI to review its own output for security issues. After generating code, prompt it with "Please review this code for common security vulnerabilities, including SQL injection, XSS, and improper authentication" and address what it identifies. This isn't a replacement for professional review, but it catches many common issues before they reach production.
- Mention OWASP Top 10 guidelines explicitly in your prompts for web applications
- Ask for input validation and sanitization for any user-facing data collection
- Request a security review as a separate step after generating initial code
Use Automated Security Scanning Tools
Several free and low-cost automated security scanning tools can catch many common vulnerabilities in AI-generated code without requiring security expertise. These tools analyze code statically (without running it) to identify patterns associated with known vulnerabilities. Running these tools before deploying any AI-generated application provides a meaningful additional layer of protection.
For dependency scanning specifically, tools like Snyk (which has a free tier), GitHub's Dependabot, or npm audit for JavaScript projects can identify vulnerable packages automatically. Many of these tools integrate directly with GitHub repositories and provide continuous monitoring, alerting you when new vulnerabilities are discovered in packages your application uses.
- Snyk: Free tier available, scans code and dependencies for vulnerabilities
- GitHub's Dependabot: Free automated dependency security updates
- OWASP ZAP: Free tool for dynamic security testing of web applications
Establish Environment Variable Discipline
One of the most preventable categories of AI-generated code vulnerabilities is credential exposure. Before deploying any AI-generated application, conduct a manual scan of all files for strings that might be credentials: look for anything that looks like a password, API key, or token. These often appear as long random strings of characters, or may begin with recognizable prefixes like "sk-" for OpenAI keys or "Bearer" for authentication tokens.
Use a .gitignore file to prevent .env files (where credentials should be stored) from being committed to version control. Enable GitHub's secret scanning feature if you use GitHub, which automatically alerts you when credentials are accidentally pushed to your repository. These practices cost nothing and prevent one of the most common and damaging categories of security incidents.
- Search all code for strings matching common credential patterns before deployment
- Enable GitHub secret scanning on any repository containing your application code
- Use a password manager or secrets vault for all application credentials
Consider a One-Time Security Consultation
For applications that will handle any moderately sensitive data, consider investing in a single security consultation with a professional. This doesn't require an ongoing engagement; a few hours with a freelance security engineer to review your application before launch can identify critical issues that automated tools miss. Many security professionals are willing to work with nonprofits at reduced rates, and some cybersecurity firms have nonprofit programs.
Organizations like the Cyber Peace Institute and the Center for Internet Security offer resources specifically for nonprofits. Some cybersecurity companies partner with nonprofits to provide pro bono security services. This is also an area where a board member with technology expertise or a relationship with a technology company might be able to help. The cost of one security review is almost always less than the cost of responding to a single breach.
- Cyber Peace Institute offers cybersecurity support specifically for nonprofits and NGOs
- Tech Impact provides affordable IT support and cybersecurity guidance for nonprofits
- Board members with technology backgrounds may be able to provide or connect you with review resources
Building an AI Security Culture in Your Organization
Individual technical practices matter, but organizational culture matters more. A nonprofit where staff understand that security is everyone's responsibility, where leadership takes data protection seriously, and where there are clear guidelines about when AI coding tools are and aren't appropriate is far more protected than one that relies on technical controls alone.
As you develop your AI compliance policies, consider including explicit guidance on AI-generated code. Define what types of data can and cannot be used in AI-generated applications without professional security review. Require that any AI-generated code used for data collection go through a documented review process, even if that process is relatively simple. Make security considerations part of the approval process for technology projects, not an afterthought.
Staff training is also essential. Many security incidents in nonprofits start not with sophisticated attacks but with simple mistakes: a staff member shares an application with a vendor without checking what data it has access to, or deploys an AI-generated tool to collect sensitive information without telling leadership. Regular, accessible training on data handling and security basics, tailored to non-technical audiences, creates the organizational awareness that makes technical controls effective. The broader challenge of securing AI tools without an IT department requires both technical safeguards and cultural foundations.
Minimum Security Standards for AI-Generated Code Projects
Establish these standards before any AI-generated application goes into production
- Data classification review: Document exactly what data the application will collect and store, and verify this is appropriate for AI-generated code
- Credential audit: Manually search all code for hardcoded credentials before any deployment
- Dependency scan: Run automated vulnerability scanning on all package dependencies before deployment
- Access control test: Verify that role restrictions are enforced on the server side by testing with accounts that shouldn't have access
- Incident response plan: Establish a clear process for responding if the application is compromised or data is leaked
- Ongoing monitoring: Set up alerts for dependency vulnerabilities and unusual access patterns after deployment
Conclusion
AI coding tools represent a genuine democratization of software development, and nonprofits stand to benefit enormously from them. The ability to build custom tools that fit your specific workflows, without hiring expensive developers or compromising on what you actually need, is transformative for resource-constrained organizations. But this capability comes with real security responsibilities that the enthusiastic coverage of vibe coding often overlooks.
The security risks of AI-generated code are not hypothetical. Vulnerabilities appear in a substantial fraction of AI-generated applications, and nonprofits lack the security infrastructure to catch and fix them that larger organizations take for granted. When your organization handles donor financial data, client health records, or beneficiary personal information, these risks have real consequences for real people.
The right response isn't to avoid AI coding tools entirely, but to use them thoughtfully. Build internal tools with limited sensitive data exposure using AI-generated code. Use established, security-audited platforms for applications that handle sensitive client or financial information. When you do build with AI tools, apply security-focused prompting, run automated scanning tools, and consider a professional review for anything that will handle moderately sensitive data. Make security part of your AI governance framework, not an optional step at the end of a project.
Developing a clear organizational framework for when AI coding tools are and aren't appropriate, as part of your broader AI strategy, is one of the most important technology decisions your organization can make in 2026. The benefits of these tools are real, and so are the risks. Understanding both is how you capture the opportunity while protecting the people who trust you with their information.
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