Seven recurring security weaknesses show up in AI-generated code often enough that every reviewer should check for them: string-built SQL, unsafe execution or rendering of model output, weak cryptography, missing authorization checks, hardcoded secrets, hallucinated or unvetted dependencies, and generated code merged without adequate review. This article gives you a checklist for each one, a review sequence, and a comparison of the tools that help.
The list is a reviewer’s synthesis of published OWASP guidance. It is not an audit of a codebase, and it does not rank which pattern is most common. Each point below links back to the OWASP document it draws on, so you can check the source yourself.
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What this list is and what it is not
The patterns come from OWASP materials on secure AI-assisted development, output handling, and code review, all current as of their 2025 publication dates. OWASP frames the core risk as inappropriate trust in generated output. Its Top 10:2025 Next Steps material (item X03:2025, Inappropriate Trust in AI Generated Code) recommends that developers understand and review the code they commit. The guidance states it directly:
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OWASP’s materials are guidance and checklists. They do not measure how often each pattern appears in real projects, so this list does not establish frequency or severity order. Treat it as a set of places to look, not a statistical picture of AI-generated code. The list is also not exhaustive; other weaknesses exist, and these seven are the ones the cited guidance names most directly.
The seven patterns and how to catch them
1. SQL injection through string-built queries
Generated code often builds SQL by joining user-controlled values into the query text. OWASP’s AI coding guidance explicitly lists SQL string concatenation as an insecure pattern, and its output-handling guidance warns that LLM-generated SQL executed without parameterization can lead to SQL injection.
What to check: every place a request parameter, form field, file value, or model response becomes part of a query string, including f-strings, + concatenation, and template literals.
How to catch it: trace the data flow from input to the database call, and require parameterized queries or prepared statements.
// Vulnerable: input is part of the SQL text
cursor.execute(f"SELECT * FROM users WHERE email = '{email}'")
// Safer: the driver sends the value separately
cursor.execute("SELECT * FROM users WHERE email = ?", (email,))2. Unsafe dynamic execution or rendered output
Generated or model-derived strings sometimes reach code paths that execute or render them. OWASP documents remote code execution from direct shell or eval use, cross-site scripting from rendered JavaScript or Markdown, and path traversal from unsanitized file paths.
What to check: any path where a string from a model, a user, or an external service reaches exec, eval, a shell command, an HTML or Markdown renderer, or a file-system call.
How to catch it: confirm validation happens at each boundary and that output is encoded for its context (HTML, attribute, URL, or shell). Model output should be treated like input from another user, not trusted because an assistant produced it.
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Generated examples sometimes use MD5, SHA-1, DES, or ECB mode. OWASP cites these as examples in its AI-assisted development guidance.
What to check: the algorithm, its purpose, and how keys are created and stored. An MD5 checksum used only to detect accidental file changes is a different problem from MD5 used to hash passwords or sign tokens.
How to catch it: search for these primitives and read the surrounding code before deciding whether it is a defect. Do not assume every occurrence has the same impact.
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4. Missing authorization checks
Generated endpoints and workflow steps often verify that a user is logged in but not that the user may act on the specific resource. OWASP lists missing authorization checks on sensitive endpoints as an insecure generation pattern.
What to check: every sensitive endpoint, admin action, and multi-step workflow. Authentication proves identity; authorization decides whether that identity may perform this action on this object.
How to catch it: for each route that reads or changes data, find the check that ties the requesting user to the resource (for example, an ownership comparison), and test with a second user’s identifiers. OWASP recommends reviewing generated code with the care you would give an unknown external contribution.
5. Hardcoded credentials and secrets
Tokens, keys, and passwords can appear in source files, notebooks, configuration, and commit history. OWASP also warns that coding assistants may read broader project context, so secrets in an open IDE workspace can end up in generated output.
What to check: source, notebooks, config files, and commits for credential-like strings. Confirm that .env files and private keys are not exposed to an active assistant session.
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How to catch it: run secret scanning on the repository and on new commits. Move credentials to environment variables or a dedicated secret store, and rotate any value that has already been committed.
6. Hallucinated or vulnerable dependencies
Models sometimes suggest package names that do not exist. OWASP describes attackers monitoring those non-existent names and registering them with malicious payloads, so a plausible-looking install command can pull in hostile code. Model knowledge may also lag newly disclosed vulnerabilities.
What to check: every new dependency in a generated change, including its exact name and spelling.
- Confirm the package exists in the registry your project uses before installing it.
- Inspect its maintainers, publication history, and source repository.
- Pin the version and review the lockfile diff.
- Run dependency auditing on the full dependency tree, not only the new package.
7. Generated code merged without adequate review
The larger issue is volume. When generated changes arrive faster than people can read them, the other six patterns are more likely to slip through. OWASP’s guidance treats this as a review-capacity problem.
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What to check: whether the change has a named human reviewer who can explain what it does, and whether security-sensitive files (authentication, payments, data access, file handling) got additional scrutiny.
How to catch it: apply the same SAST, software composition analysis, and secret scanning gates you use for human-written code. Automated tools find risky patterns, but they do not judge whether an authorization decision or business rule is correct.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical review sequence
- Start with the changed files and mark their trust boundaries: where user input, external services, or model output enter the code.
- Trace those inputs to sensitive sinks: database calls, shell execution, HTML or Markdown rendering, file access, authentication and authorization decisions, and package installation.
- Run SAST, dependency analysis, and secret scanning with the same thresholds you apply to human-written code.
- Review each finding in context. A flagged line is a question about data flow, not a verdict.
- Verify every new dependency against the registry before it is installed, then pin it.
- Require a human owner to explain and approve the final change.
OWASP’s output-handling guidance (LLM05:2025, Improper Output Handling) recommends validating model output before backend use, encoding it for its output context, parameterizing database operations, and monitoring unusual output patterns. These are standard secure coding practices. The review target is the data flow and trust boundary, not whether a model wrote the code.
Choosing review methods
No single method covers all seven patterns. OWASP’s Secure Code Review Cheat Sheet describes manual review as complementary to automated testing rather than a replacement for it. The main methods cover different ground:
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| Method | What it checks well | What it misses |
|---|---|---|
| SAST (static analysis) | Code patterns such as string-built queries and insecure function calls | Whether an authorization decision is correct for the business |
| Software composition analysis | Known vulnerabilities in third-party dependencies | Packages that do not exist, or malicious packages with no published advisory |
| Secret scanning | Credential-like strings in source, config, and commits | Secrets in formats the scanner does not recognize, and logic flaws |
| Manual review | Business logic, authorization, and trust boundaries in context | Scale; it depends on reviewer time and attention |
Teams can also choose between a baseline review of the whole application and a diff-based review of changes. A baseline review suits a first audit or a major rewrite. A diff-based review suits routine pull requests, where the goal is to catch what the change introduced.
Extra care for agentic coding tools
When an AI tool can run commands, edit files, or call external services, the review problem extends beyond the diff. OWASP’s secure-coding guidance recommends running agents in a sandbox, granting least privilege, using scoped credentials, and keeping reviewed allowlists for tools and MCP servers. The same guidance recommends managing dependency audits and version pinning for any package an agent installs.
Further reading
OWASP’s Web Security Testing Guide v4.1 suggested reading list names The Web Application Hacker’s Handbook: Finding and Exploiting Security Flaws, 2nd Edition, by Dafydd Stuttard and Marcus Pinto (Wiley, ISBN 9781118026472). It is a general web-application security reference, not a guide to AI-generated code.
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