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Is AI-Written Code Safe? Risks and How to Review It

AI-written code often runs on the first try. That proves it runs, not that it is safe. Treat it like code from a new contractor you have never worked with: useful, and reviewed before it ships.

Updated October 20, 2026

Safe enough, with review

AI coding assistants produce likely code, not guaranteed secure code. They learned from public examples, and many examples are simplified, outdated or missing safeguards. Code that a person has reviewed, tested, dependency-checked, scanned for secrets and deployed with limited permissions can be fine. Code pasted straight into a live product is a business risk: data exposure, downtime and emergency fixes.

Insecure patterns AI repeats

  • SQL injection: user input pasted into a database query, so a crafted search or login string becomes a command. Look for queries built by joining text; use parameterized queries.
  • Missing authorization: the code checks that someone is logged in but not that the invoice at /invoice/1007 is theirs. Ask: what stops user A from reaching user B's data?
  • Hard-coded secrets: a placeholder API key replaced with a real one and committed. Keys belong in a secrets manager.
  • Weak cryptography: outdated password hashing, predictable reset tokens or home-made encryption. Use current, well-maintained libraries.

Invented packages and slopsquatting

AI tools sometimes recommend software packages that do not exist. Attackers register those names with malicious code and wait for someone to install them, a trick nicknamed slopsquatting. A USENIX Security 2025 study tested 16 code-generating models on 576,000 samples and found at least 5.2% of suggested packages from commercial models and 21.7% from open-source models were hallucinated, with 205,474 unique made-up names.

Before installing anything an AI suggests, confirm the package is real, maintained and widely used for that purpose. Be most careful with copy-paste install commands.

Outdated libraries and licenses

Assistants trained on older examples may suggest old versions or libraries that have been replaced. Your dependencies are part of your product: a known flaw in one becomes your flaw.

Every dependency also carries a license. Some require notices or sharing source code. A surprise license can delay a launch or complicate a sale of the business, so record dependencies and review their terms before release. Our AI copyright guide covers ownership of AI output.

"It runs" is not "it is safe"

A demo proves the happy path. It says nothing about missing or malicious input, expired sessions, duplicate requests, oversized uploads, failed payments or a user poking at someone else's data. Test those on purpose.

A review process small teams can run

  1. Human review by someone who understands what the code does, what data it touches and who can reach it.
  2. Automated security scanning for injection, unsafe functions and weak crypto.
  3. Dependency checks and an SBOM, a list of every component, so you can react when a library reports a flaw.
  4. Failure-case tests: unauthorized users, bad input, expired sessions, network errors.
  5. Secret scanning of code, configs and logs; rotate any real secret found, do not just delete it.
  6. Least-privilege deployment, so a flaw has little it can reach.

The same rules apply to AI features inside your app; see the OWASP Top 10 for AI apps.

Someone has to own the code

AI is a drafting partner, not an authority. Assign a person who understands each change, approves its dependencies, checks licenses and decides it is ready. Ask the AI for alternatives, explanations and tests, then verify with human judgment and tools. Our guide on vibe coding covers the risks of shipping code nobody read.

Where ASL fits

At ASL, every release is rebuilt and compared with what is running before it is called live, and test suites gate every deploy. If your team has built something quickly with AI and wants a second pair of eyes, our custom software development team can review it for security, dependencies and failure cases. See also security-first app development. Contact us to talk it through.

Frequently asked questions

Is AI-generated code safe to use?

It can be, after human review, security scanning, dependency checks, failure-case tests and secret scanning. Never ship it unread.

What is slopsquatting?

Registering package names that AI tools hallucinate and filling them with malicious code, so anyone who installs the suggested package is compromised.

How often do AI tools invent packages?

A 2025 USENIX Security study found at least 5.2% of packages suggested by commercial models and 21.7% by open-source models did not exist.

What is an SBOM?

A Software Bill of Materials: a list of every component in your software, so you can respond quickly when one reports a vulnerability.

What is the most common security gap in AI-written code?

Missing authorization checks are among the most damaging: code confirms a user is logged in but not that they may see that specific record.

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