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AI coding assistants can produce code that looks polished yet is inaccurate, outdated, incomplete, or insecure. The reliable way to use one is to treat every suggestion as an unverified draft: inspect it in the project’s context, run tests, apply automated analysis, and obtain human review when the change matters.
There is no single, current error-rate number that applies to every assistant, language, model, or task. Evidence from interviews and controlled studies explains common failure patterns and useful checking practices, but it does not certify any particular tool or provide a universal percentage.
Why does AI-generated code have mistakes?
An assistant predicts a likely continuation from its training and the context it receives. That process can produce useful boilerplate, but plausibility is not proof that the result matches your repository, dependency versions, business rules, threat model, or edge cases.
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The assistant lacks important project context
Code rarely stands alone. A function may depend on an internal convention, an undocumented database constraint, a feature flag, an authorization rule, or a caller elsewhere in the repository. If those details are not in the prompt or available to the assistant, it may fill the gaps with a reasonable-looking assumption. Professionals interviewed in a qualitative study reported that assistants often did not know enough of the surrounding code, and that this missing context made suggestions harder to trust.
Context also includes negative requirements. The assistant may know that a framework supports a particular API but not that your project has deliberately disabled it, must preserve backward compatibility, or handles errors through a central middleware layer. A snippet can therefore be syntactically valid while violating an architectural or product requirement.
Its information can be stale or inaccurate
Libraries change names, defaults, authentication flows, and deprecation status. Training data may describe an older release, a different language version, or an API that never existed in the form suggested. Interview participants reported outdated and inaccurate output. Check the documentation for the exact dependency and version used by your project instead of accepting an invented method or option because its name sounds familiar.
It can hallucinate APIs, files, and explanations
A fluent model can invent a package, configuration key, endpoint, exception class, or citation. It may also explain an incorrect implementation with confidence. The explanation is generated text, not an execution trace. Verify that every imported symbol exists, every file path is real, and every claimed behavior is demonstrated by documentation or a test.
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Participants in the qualitative study described lower-quality results for larger code blocks and more complex problems. As the requested change spans more modules, the assistant has more interactions to preserve and more assumptions to get wrong. A response can be locally coherent while failing to update a related interface, migration, test fixture, or error path.
Breaking a request into smaller, reviewable changes can make omissions easier to see. It does not make the output correct automatically; it simply gives you narrower units to inspect and test.
Security requirements are easy to omit
Generated code may implement the happy path without input validation, authorization checks, safe secret handling, or careful error behavior. Professionals reported concern that assistants could omit protections or introduce vulnerabilities. Those findings describe risks people encountered or anticipated; they do not mean that all generated code is insecure or provide a universal vulnerability rate.
Human fluency bias hides defects
Names, comments, formatting, and a confident explanation can make a suggestion feel reviewed before anyone has run it. Microsoft Research describes code completions as potentially inaccurate and capable of introducing bugs or security vulnerabilities if a programmer does not detect and correct them. Treat readability as a maintainability benefit, not as evidence of correctness.
What the available studies actually show
Two sources provide useful but deliberately limited evidence. A Microsoft Research paper published in April 2025, “Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions,” reports a mixed-methods study involving 30 programmers. It examined different ways to highlight parts of a completion that might need attention.
A separate qualitative study presented at ACM CCS 2024 interviewed 27 software professionals about AI assistants, security practices, and concerns. Participants described inaccurate, outdated, and hallucinated output; difficulty supplying enough project context; and weaker results for complex or large requests. They also described manual inspection, adapting and executing suggestions, software testing, static analysis, fuzzing, and peer review as parts of their checking process.
These sample sizes are counts of people in those studies, not statistics about all developers or all AI-generated code. The studies do not establish one current error rate, isolate one universal cause, rank assistants, or show that every model behaves in the same way.
How can I tell if AI-generated code is wrong?
Look for evidence that the suggestion fits the actual requirement, repository, runtime, and risk. The following signs are useful prompts for investigation, not proof by themselves.
- Names or interfaces do not line up: an import, method, parameter, return type, schema field, or configuration key differs from the definitions used elsewhere in the project.
- The code assumes a version: it uses a deprecated option, a newly introduced feature unavailable in your locked dependency, or a behavior that changed between releases.
- It handles only the happy path: malformed input, empty collections, timeouts, retries, partial failure, duplicate requests, cancellation, and permission failures receive no defined behavior where those cases matter.
- It changes more than requested: unrelated formatting, dependencies, data structures, or configuration changes increase the review surface and may conceal an accidental behavior change.
- Comments promise more than the code does: a comment says a value is sanitized, atomic, authorized, or encrypted, but the implementation does not demonstrate that property.
- Errors are swallowed: broad exception handling, ignored return values, default credentials, or fallback values can turn a visible failure into silent corruption.
- Security boundaries are unclear: user-controlled data reaches a query, command, template, filesystem path, or HTML output without an explicit protection appropriate to that boundary.
- Tests are conspicuously absent: a substantial behavior change arrives with no updated tests, or the proposed tests assert only that a function does not crash.
- The explanation relies on unverifiable claims: references to an API, benchmark, or framework behavior cannot be found in the versioned documentation.
How do I check code written by an AI coding assistant?
Use a repeatable loop. The amount of checking should match the consequence of failure: production authentication, payments, data deletion, infrastructure, and safety-related code deserve more independent scrutiny than a disposable local script.
- State the requirement and boundaries. Write down the intended behavior, inputs, outputs, supported versions, performance or compatibility constraints, and what the change must not alter. Identify security and privacy requirements before reviewing the generated text.
- Read the complete diff in context. Inspect every changed line and the surrounding callers, interfaces, schemas, configuration, and tests. Confirm that names and types match the repository. Look for files the assistant should have changed but did not, as well as unrelated edits it introduced.
- Verify external facts. Check imports, methods, flags, and configuration against the exact dependency and language documentation used by the project. Confirm that a claimed default or behavior is documented rather than inferred from the assistant’s explanation.
- Run the smallest relevant test first. Execute the project’s documented unit or component tests for the changed behavior in an appropriate, isolated environment. Fix setup errors separately from product failures so a passing command is not mistaken for a meaningful test.
- Exercise normal and edge cases. Test valid inputs, boundary values, empty and malformed inputs, repeated calls, failures from dependencies, permission differences, timeouts, and concurrency where applicable. Compare observed results with the requirement, not merely with the assistant’s predicted output.
- Add automated analysis suited to the code. Run the language compiler or type checker, linters, static analysis, and security scanning that your project uses. For parsers, protocol handlers, or other input-heavy code, consider fuzzing. These checks find different classes of problems and none proves the absence of defects.
- Review the security assumptions explicitly. Trace untrusted data, authentication and authorization decisions, secrets, cryptography, file and network access, logging, and error responses. Ask what happens when an attacker controls each input or when a dependency fails.
- Obtain peer review before consequential integration. A reviewer should understand the requirement and inspect the AI-assisted change as ordinary production code. Peer review is an additional control, not a guarantee that every defect will be found.
- Record what was checked. Keep the tests, analysis results, version information, and any manual decisions with the change. This makes later maintenance possible when a dependency or threat changes.
Which verification method should I use?
Different checks reveal different failure modes. The qualitative study reports these practices, but it does not measure their relative effectiveness or recommend a particular vendor.
| Method | What it can reveal | When it fits | What it cannot establish alone |
|---|---|---|---|
| Manual inspection and diff review | Wrong assumptions, missing context, unintended edits, mismatched interfaces, and suspicious control flow | Immediately after generation and before committing a change | That all runtime paths work or that a subtle vulnerability is absent |
| Unit and integration tests | Behavioral failures for the cases represented by the tests | After implementation and whenever behavior or interfaces change | Correctness for untested inputs, environments, or threat scenarios |
| Compiler, type checker, and linter | Syntax, type mismatches, unreachable or suspicious constructs, and project-style violations, depending on configuration | As a fast local or continuous-integration gate | Business-rule correctness or security in code that passes the configured rules |
| Static analysis and security scanning | Some suspicious data flows, unsafe patterns, dependency issues, and policy violations | During development and before merge, with rules appropriate to the language | A complete security audit or proof that a reported finding is exploitable |
| Fuzzing | Crashes, hangs, parser errors, and unexpected behavior exposed by generated or mutated inputs | Input-heavy components and protocols that can be exercised repeatedly | Requirements that are not encoded in the fuzz target or oracle |
| Peer review | Design assumptions, maintainability concerns, missed requirements, and risks a second person recognizes | Before merging changes with material operational, security, or user impact | Exhaustive coverage or immunity from reviewer error |
How should security review differ for AI-assisted code?
Security review should begin with the boundary the code crosses, not with whether the snippet looks idiomatic. Identify every value that can be influenced by a user, another service, a file, an environment variable, or a compromised dependency. Then verify the control that limits that value’s effect.
Input and output handling
Confirm that validation is appropriate to the operation and that encoding or parameterization occurs at the correct sink. Do not treat a comment such as “sanitized input” as evidence; follow the value through the actual call chain. Check error messages and logs for secrets or sensitive data.
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Verify authentication separately from authorization. A handler that confirms a user is signed in may still fail to check whether that user can access the specific record or operation. Test denied and cross-tenant cases, not only a successful request from an administrator.
Secrets and cryptographic operations
Look for credentials embedded in source, unsafe defaults, accidental logging, weak key or nonce handling, and custom cryptography where a maintained library is expected. Confirm how secrets are supplied in each environment and how they are rotated or revoked.
Failure and dependency behavior
Inspect timeouts, retries, transaction boundaries, cleanup, and partial failures. An assistant may add an automatic retry that duplicates a non-idempotent operation or catch an exception that should trigger a rollback. Test dependency outages and permission failures where they affect data integrity.
The 2024 qualitative study supports giving these questions special attention because participants reported concerns about omitted protections and introduced vulnerabilities. It does not provide a complete vulnerability checklist or test result for each class of flaw.
Can an uncertainty or confidence indicator tell me whether code is safe?
Not by itself. The Microsoft Research 2025 study compared three completion displays: no highlighting, highlights for tokens with the lowest model-generation likelihood, and highlights for tokens predicted most likely to be edited by a programmer. Highlights based on predicted human edits led participants to finish tasks faster and make more targeted edits. Highlights based only on generation probability did not outperform the no-highlight baseline.
Participants preferred cues that were granular, informative, interpretable, and not overwhelming. The finding concerns particular display designs and task performance. It is not evidence that a confidence score can certify generated code as correct, secure, or suitable for production. Treat any indicator as a way to prioritize inspection, then apply tests and review.
How should teams handle proprietary code and confidential data?
Check organizational policy before sending source code, logs, credentials, customer data, or unreleased product details to an external assistant. Participants in the qualitative study identified privacy and data-leakage concerns and noted that workplace policies affect how assistants are used.
Minimize the context you share, remove secrets and personal data, and use approved access controls and retention settings where your organization provides them. If a task requires information that policy forbids sharing, solve it locally or ask for a redacted, abstracted example instead. Keep a record of any required approval for sensitive use.
What does responsible use look like in practice?
Use the assistant for a bounded change with an explicit acceptance test rather than asking it to redesign an entire system without checkpoints. Supply the relevant interfaces, constraints, and version information, but do not assume that more pasted code removes the need for review. Ask for a plan and a small patch when the change is complex, then verify each part before proceeding.
Separate generated text from accepted code in your workflow. Review the diff, run the same gates used for human-written changes, and preserve a clear author or change record according to team policy. A human remains accountable for deciding whether the implementation meets the requirement and is safe to deploy.
One participant, identified only as P14 in the qualitative study, said: “I just think there are vulnerabilities and there are things that it doesn’t know. Currently, and at least for the next five years, I think all code written by AI will need to be gone over by a professional.” This is P14’s individual opinion and forecast, not a conclusion by the study authors or a proven prediction. Its practical implication is narrower and actionable: professional review remains appropriate for code whose failure would matter.
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