If you are building with AI, security is the risk most likely to hurt you and least likely to show up in a demo. AI-generated code looks fine and frequently is not. Here are the specific risks and how to manage them.
The short answer
AI-generated code carries a high rate of security vulnerabilities, because the model does not understand your application's risk model. Manage it with human security review, automated security testing in your pipeline, careful dependency checks, and never shipping unreviewed AI output for anything handling user data, auth, or payments.
The specific risks
- Common vulnerabilities. A majority of AI-generated samples have been found to contain flaws, including weaknesses against cross-site scripting and injection attacks.
- No risk-model awareness. AI does not know your threat landscape, your data sensitivity, or your internal standards, so it omits controls it was not explicitly told to add.
- Hallucinated dependencies. AI can invent package names that do not exist; installing them creates supply-chain holes that scanners may miss.
- Volume over scrutiny. Because AI produces so much code so fast, it rarely gets the line-by-line review hand-written code would.
- Inconsistent controls. Security applied unevenly across a codebase leaves gaps.
How to manage them
- Human security review. Someone who understands security reads anything sensitive before it ships.
- Automated security testing in CI. Catch issues early, when they are cheapest to fix.
- Verify dependencies. Confirm packages are real, maintained, and reputable before installing.
- Be strict around sensitive areas. Auth, payments, and user data get extra scrutiny, no exceptions.
- Never ship unreviewed AI output. Treat it as a draft, always.
The bottom line
AI is a powerful accelerator, but on security it is an unreliable narrator. The fix is not to avoid AI; it is to keep experienced judgment and real security practice in the loop (see is AI-generated code safe).
That is exactly how a product simulation is built: AI provides speed, but the foundation is reviewed and structured with security in mind, so what you build on is sound, not a pile of plausible-looking vulnerabilities.
Want AI speed without the security gamble? Start a project.
Frequently asked
How common are vulnerabilities in AI-generated code?
Studies have found a majority of AI-generated code samples contain design flaws or known vulnerabilities, even from current top models. It is common enough that you should assume review is required, not optional.
What is a hallucinated dependency?
AI sometimes references software packages that do not actually exist. If a developer installs one to make the code run, attackers can register that name with malicious code, a real and sneaky supply-chain risk.