Somewhere around 2022, the conversation in most engineering orgs quietly shifted. It wasn't a memo, it was just that suddenly everyone had a tab open with GitHub Copilot, and junior devs were shipping features faster than seniors who'd been there for years. AI coding assistants didn't knock on the door; they walked straight in through the kitchen.
The productivity gains are real. But here's what's getting buried under the enthusiasm: these tools have a security problem. Not a theoretical one, but a practical, showing-up-in-production-code problem. This guide covers where those risks come from, what they look like, and what you can do about it to prevent AI generated code vulnerabilities.
What Is AI-Generated Code?
Large language models use vast amounts of source code, including GitHub, Stack Overflow, and other resources to train themselves to generate AI-based code. These models are not able to understand code in the same way as a human; they only learn patterns and predict the likelihood of tokens being used together based on past usage. Various code generation tools such as GitHub Copilot, Amazon CodeWhisperer and Tabnine provide this functionality within the code editor itself. Developers are utilising these tools for two main reasons; an increased speed of development and to reduce cognitive load when switching contexts. The downside to this increased efficiency is that developers are more likely to use the tools' suggestions without taking the time to properly review those suggestions.
Why AI-Generated Code Can Introduce Vulnerabilities
The underappreciated issue is the problem of training data. These models have learned from millions of real-world code bases, but they have also learned from millions of real-world security mistakes. Outdated authentication patterns, deprecated cryptographic functions, and SQL queries created via string concatenation (and/or assembly) all contribute to this. Models reproduce what they have learned, and the weight given to their output is based on frequency rather than correctness.
The second major gap is context. The model does not know your threat model, your compliance requirements, or that this particular endpoint is publicly accessible on the web. It sees only the code immediately surrounding the cursor and has to guess at the best solution available. When you factor in both the deadline pressure and the general tendency to trust syntactically correct output, you have a system that consistently produces vulnerabilities as a matter of routine.
Common Vulnerabilities Found in AI-Generated Code
- Embedding API keys, passwords, and tokens in source code.
- SQL injection when query strings are combined appropriately vs. parameterized queries.
- Command injection when user input gets executed with shell commands; no validation happens to the input first.
- Missing security controls on sensitive APIs.
- Having broken access controls since users are able to assume roles without being verified.
- Using old cryptographic algorithms (for example, using MD5 or SHA1 for password hashing).
- Using old versions of software packages - these will include packages with known CVE-based issues that would be considered best practices for avoiding those issues.
- Verbose error messages that expose debug-based information about the application (for example full stack trace) or expose sensitive information about the application (such as the location of files used by the app).
- No validation of input since malformed input can be delivered to the application for processing.
- Cross site scripting (XSS) through use of output in unescaped HTML template files.
Key Vulnerability Areas
- Unsecured Authentication & Authorization
AI systems will generate auth checks that seem perfectly normal but many will contain significant issues, especially in how they handle valid token verification w/ no expiration checks. Password comparison will use equality operators instead of constant time functions, and many systems will use a Math.random() session token, leaving an open security surface.
Access control is consistently a fail in these systems. Confirming that a JWT is valid with no follow up check to see if the UserID matches the resource is an open access control failure and will lead to abuse.
- Injection Attacks
SQL injects should be a solved problem as we have had parameterised queries for decades, yet AI assists will provide SQL created via string formatting, such as f-string in Python or template literals in Javascript, as this is considered 'normal' in many older tutorials. Command injection follows the same flow with a greater blast radius, especially if os.system or subprocess with shell=True is used with user-provided input, enabling remote code execution.
- Hard-coded secrets
When you ask an AI Assistant to help you connect to a database, there’s a strong chance that it will produce the credential in-line, e.g., database_password = “my_password” with a comment to change that later. That change seldom happens, so secrets make their way into git history, Docker images and deployment artifacts before they are deployed into production. The training data for these AI assistants is filled with examples where getting something running trumped securing it.
- Insecure Dependencies and Poor Error Handling
AI assistants have a knowledge cutoff, and their knowledge of library versions that have CVEs may be inconsistent (i.e., an AI assistant could provide you an import statement for a package that has a critical CVE but was patched 6 months ago, and that looks fully authoritative from the auto-complete list). Error handling compounds the issues with AI-generated catch blocks tending to be verbose (e.g., including stack traces in response bodies and exception messages containing file and schema names), which can provide “reconnaissance gold” for someone trying to map out your architecture prior to attempting an attack.
How to Prevent AI-Generated Code Vulnerabilities
- Always check AI-created code
Your most significant shift in perspective should be to view all AI proposal drafts rather than final products or endpoints. Therefore, we need to promote code review as a culture that explicitly encompasses any code created by AI, as there should be greater scrutiny of AI suggestions than of human suggestions.
- Adhere to secure coding principles
Performing software development using OWASP secure coding standards may not be sexy, but it has been proven effective in a combat environment. Including these standards in your development principles — within pull requests, education for new hires, and code review — allows you to compare AI-based proposals with established security standards. If a Copilot proposal does not meet the standards defined by OWASP or any other formal coding standard, the standard must prevail.
- Employ Automated Security Scanning Tools
SAST tools provide a means for reviewing codes before they run so they can identify approximately 90% of the different vulnerability classes associated with AI-generated codes. To go further, DAST tools test the delivered code against numerous known attack vectors (such as SQL injection attacks) once the code has been run. However, neither method will replace human review processes for identifying vulnerabilities; rather, implementing both together will reduce the chance that your production never contains a SQL injection, hard-coded secret, etc., to undetected state.
- Confirm Third-Party Dependencies Are Safe
Before running CI, you should run dependency scanning tools (e.g., Snyk, Dependabot, OWASP Dependency-Check), which will give you real-time feedback about whether the package recommended by an AI will contain known vulnerabilities (or any transitive dependencies). Making this process automatic removes the need for each developer to have up-to-date knowledge of the CVEs of all the libraries used in their development environments.
- Avoid Hardcoding Secrets
Secrets must not reside “hard-coded” within your application code; instead, they should be stored in a secret manager (e.g., AWS secrets manager, HashiCorp vault) or in environmental variables retrieved from a secure .env file that is not tracked by version control. There are tools available (GitGuardian & truffleHog) that can scan repositories and CI pipelines for any credentials previously committed to source control. The process of developing an application must provide the secure method as the easy method; otherwise, developers under pressure will opt for the easy method.
- Implement Secure Input Validation
Input from untrusted sources must be validated against pre-defined rules before it can be utilized by the business logic of your application, regardless of whether that input originates from user-submitted forms, API parameters or headers, or URL segments. Validation can include checking that the data type is as expected, that it meets length restrictions, that it is in the correct format, and that it is validated in accordance with where it is being sent. Any AI-generated suggestion must have the capability to get validated before it can be considered complete and usable.
- Monitor and Audit AI-Generated Code
Monitoring and auditing AI code is critical for detecting failures, and auditing all code originating from AI input creates an audit trail to assess if the code is being developed in a ‘safe’ way. This will assist with the security audits of high density AI generated code with greater scrutiny than code developed using human agency.
- Train Developers on AI Security Risks
Training developers on the security risks posed by AI code is critical as the tools are a development trend that has not matured much. Therefore, many developers do not have prior formal training regarding detection of failure modes of these new toolsets. A minimum single hour training providing most common flaws, public incident examples and a framework for validating AI output will be very effective for altering the behaviour of the development team.
Best Tools for Detecting AI Code Vulnerabilities
- Gomboc focuses on automated remediation of cloud infrastructure misconfigurations, using AI to not just detect security issues but generate and apply fixes. For teams operating complex multi-cloud environments, that kind of closed-loop remediation capability is increasingly relevant.
- GitHub Advanced Security combines CodeQL, secret scanning, and dependency review in one
- SonarQube provides broad language support, integrates cleanly into existing CI pipelines.
- Snyk provides real-time dependency monitoring with actionable fix recommendations
- GitGuardian scans repos and CI pipelines for leaked credentials
Conclusion
AI-powered code generation tools have helped many developers work faster and more effectively. They will continue to exist and improve upon their current level of capability. However, we must remember that developing with AI requires that we maintain secure practices through understanding AI-created code rather than blindly accepting it.
Historically, we have only seen the downside of poorly developed software due to the existence of potential security issues that have resulted from code-generation tools being poorly used. There are already examples of such vulnerabilities, including injection vulnerabilities; hard-coded credentials; broken authentication; and vulnerable libraries existing within production code because no one has examined the generated code carefully. In reality, the use of AI code-generation tools is beneficial for both productivity and security; however, in order to ensure that the two are maintained concurrently, you must develop AI code generation workflows with care.
Use Gomboc as an AI code security assistant to continuously audit, validate, and enforce secure practices across all AI-generated code.
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