AI coding assistant hallucinations are plausible-looking but false code suggestions, package names, or security advice. They matter because developers may...
AI Coding Assistant Hallucinations: The Security Risks Hiding in Your Code
AI coding assistant hallucinations are plausible-looking but false code suggestions, package names, or security advice. They matter because developers may trust polished output and run it before checking it. A fabricated dependency can become an attack path if someone registers that name and a build pipeline installs it.
Key Takeaways- AI models predict likely text; they do not automatically verify that code, packages, or security claims are true.
- A package name that does not exist today can become dangerous if an attacker registers it later.
- Review dependencies, lockfiles, authentication, cryptography, and cloud permissions before release.
- Put checks in automated workflows, where they can run every time—not just when a reviewer remembers.
- Use AI for speed, but treat its output as untrusted until checked against reliable sources.
The danger is not that every suggestion is wrong. The danger is that correct and incorrect suggestions can look equally polished, arrive in seconds, and blend into code that already works. When I review generated code, I look past whether it reads well and ask a more useful question: what evidence says this dependency or security choice is safe? Here’s the kicker: compiling code proves that a compiler accepted it, not that the code is secure or the package is trustworthy.
What Is an AI Coding Assistant Hallucination?
An AI coding assistant hallucination is an answer that looks credible but includes a false claim or an invented detail. In code, that might be a package that does not exist, a function that is not part of the library, or a security recommendation that is old or unsafe. The model may write all of it in the same calm, certain tone.
Most language models generate text by predicting likely next tokens from patterns in data. That process can produce useful code, but prediction is not the same as checking a fact. If the model has incomplete or conflicting examples, it may fill a gap with something that resembles a real API. A convincing name is easy to produce. Proof that the name is real takes a separate check.
Code makes this harder to spot than ordinary prose. Package names follow familiar patterns. A made-up function can match the style of a real software development kit. The output may even compile if a local stub or similarly named library happens to exist. Compilation answers a narrow question: can this code be translated or run in this setup? It does not establish who wrote the dependency, what its install script does, or whether the security design is sound.
Package suggestions create a special risk. A nonexistent name is not automatically malicious. But if a developer tries to install it, an attacker may register that name first and publish harmful code under it. Researchers call this kind of attack slopsquatting: instead of relying on a developer to mistype a package name, an attacker registers a name a model repeatedly invents. A study of 16 code-generating models reported that roughly one in five package suggestions in its test outputs pointed to nonexistent packages, with more than 205,000 unique fabricated names. The result is limited to the models and tests studied, not a universal rate. See the researchers’ analysis of package hallucinations for the details.
I have seen how quickly a plausible API name can pass through a busy review. A reviewer notices tidy formatting and a clean build, then moves on to the more obvious changes. Frankly, that is why a good check must ask whether the package and API exist in the first place—not merely whether the code looks like code.
Core Benefits of Taking Hallucinations Seriously
Treating AI output as untrusted does not mean banning coding assistants. It means getting the useful parts without letting speed replace judgment. A model can draft a test, explain an unfamiliar code path, or create a first version of routine glue code. Those tasks can save time when a developer checks the result and owns the final decision.
The benefits of a deliberate review process are practical:
- Fewer supply-chain surprises. Checking a package name against its registry, verifying its owner, and reviewing its release history can stop a fabricated or suspicious dependency before it reaches a build.
- Safer security choices. A person who reviews authentication, cryptography, and permissions can catch advice that sounds reasonable but does not fit current guidance or the service’s needs.
- More reliable builds. A committed lockfile and controlled dependency updates make it easier to reproduce a build and see what changed. Teams can trace a new package instead of discovering it only after a deployment fails.
- Clearer accountability. When a developer records why a dependency or security pattern was accepted, later reviewers have evidence to inspect. A confident model response is not evidence on its own.
- Better use of engineering time. Routine drafting can be fast, while automated checks handle repeatable questions such as whether an import resolves to an approved package.
I have found that the best outcome is not less code review; it is a review that spends its attention on the right risks. For example, a generated formatting helper may need a quick test, while a generated sign-in flow deserves a careful look at recovery, session expiry, and phishing resistance. Let’s be real: calling both changes AI-generated code does not make their risk equal.
This is also a matter of stewardship. Software decisions affect customers, coworkers, and people whose data a service holds. A team should use its tools in ways that respect those people, not shift responsibility to an autocomplete box. OWASP includes misinformation among the risks teams should consider in its guidance on large language model applications. The lesson is simple: output needs checking, especially when someone else will bear the cost of a mistake.
Step-by-Step Guide to Safer AI-Assisted Development
Put the checks into the workflow. A reminder to be careful is not much of a control when a developer is under deadline and an assistant has already written the command to install a package.
- Mark generated output as unverified. Treat suggested code, package names, commands, and security explanations as proposals. Make that expectation clear in team guidance and code-review templates. The aim is not to label every AI-assisted line forever; it is to prevent unexamined suggestions from gaining automatic trust.
- Verify every new dependency before installation. Search the official package registry, then check the exact package name, publisher, release history, repository, and documentation. Look for a package that has a credible purpose and maintenance history. Confirm that it is the intended library, not a near-match with an unfamiliar owner. Do not run an install command simply because the model supplied one.
- Limit what builds are allowed to fetch. Use approved package sources or dependency allowlists where they fit your environment. In continuous integration, block unexpected packages or require review for new dependencies. An allowlist can be too strict for a fast-moving project, so give developers a clear way to request a review instead of encouraging workarounds.
- Pin and review dependency changes. Keep the lockfile under version control and inspect changes to it during review. Pinning does not make a package safe, but it helps keep builds repeatable and makes unexpected updates visible. Use integrity checks where supported, and review transitive dependencies when a new direct package brings in a large dependency tree.
- Run checks in a restricted environment. Dependency installation may run scripts, and build tools can access files, credentials, or network resources. Give untrusted code only the permissions it needs. Keep secrets out of pull-request builds from untrusted branches, and use isolated runners when appropriate. I have seen teams focus on a package’s name while forgetting the more important question: what can its install or build process reach?
- Review security-sensitive code against trusted guidance. For authentication, ask how sign-in, recovery, session handling, and account changes work. For cryptography, check that the algorithms and library use are current and appropriate. For cloud access, compare every permission with the task the service must perform. Avoid accepting broad access merely because the generated example works.
- Use documentation grounding, then check the result. Retrieval-augmented generation can provide an assistant with a selected set of documentation instead of relying only on patterns learned earlier. That can reduce guesses about a specific API or version. It does not remove the need to verify the documentation date, the version in use, or the code the model produces.
- Measure what your checks catch. Track new dependencies, blocked registry lookups, review findings, and security defects tied to generated code. Run small tests on the assistant your team actually uses, such as asking it for dependency suggestions and checking whether the names resolve. A useful benchmark measures your own workflow over time; a single score should not become a promise that the output is safe.
These steps fit established secure-development work. NIST’s Secure Software Development Framework describes practices teams can build into their development process. Here’s the practical bit: do not create a separate ceremony for AI. Add checks to the places where code already enters—dependency review, CI, security tests, and release approval.
Comparison Table: AI Coding Assistants vs. Traditional Autocomplete
Traditional autocomplete and AI coding assistants both suggest code, but they do not behave in exactly the same way. Traditional autocomplete commonly offers names or snippets based on the current file, project symbols, or a known catalog. An AI assistant can draft a larger block and explain it in natural language. That broader reach can help with routine work, but it also gives the model more room to invent details that sound right.
| Factor | AI coding assistant | Traditional autocomplete |
|---|
| Typical output | Code blocks, explanations, APIs, and dependency suggestions | Symbols, method names, and short code completions |
| Main source of error | Plausible but false code, package names, or security claims | Incorrect or irrelevant completion, often tied to local context |
| Package risk | May suggest a convincing package name that needs registry verification | Usually suggests known or locally available names, but still needs checking |
| Security judgment | Can produce a full design that sounds current without proving it is safe | Usually offers less design advice, so it may create fewer broad security claims |
| Review need | Check code, dependencies, versions, and security assumptions | Check the completion in context and test the result |
| Best use | Drafting and explaining work with a human responsible for approval | Speeding up routine typing and use of known project symbols |
The comparison is not a claim that autocomplete is safe by default. A completion can still be wrong, and an existing package can still contain vulnerable code. The difference is the size and confidence of the answer: an assistant may write a complete authentication flow, while a conventional suggestion may offer a single method name. That can change how much review the result needs.
I use a simple rule: the wider the assistant’s claim, the wider the check. A short completion in a well-known local function may need a quick test. A new package, cryptographic choice, or cloud policy needs evidence from the registry, documentation, or a security reviewer. The package-hallucination findings are a reason to check AI-generated dependencies, not a reason to assume every recommendation is harmful. The truth is, the tool type matters less than whether your process verifies what it introduces.
Common Mistakes to Avoid
The first mistake is treating a successful build as a security review. A compiler can confirm syntax and types; it cannot tell you that an attacker owns a package or that an access policy grants too much. Tests are useful, but tests only cover the cases they were written to check. A green build is one signal, not a safety certificate.
The second is trusting a package because its name looks familiar. Attackers can choose names that fit common naming patterns, and a model can invent names that look like ordinary libraries. Check the exact registry entry, ownership, and documentation. If the package does not exist, do not fix the problem by installing a similarly named one without understanding the change.
Another bad habit is relying on a prompt to make the answer secure. Asking an assistant to write secure code can help it focus on that goal, but the request does not turn it into a verifier. Models can repeat outdated advice or invent details even when the prompt is careful. I once saw how a clean explanation can make a weak design feel settled before anyone had asked what happens during account recovery. Here’s the kicker: better wording can improve an answer, but it cannot substitute for review.
Teams also make the opposite mistake: they tell people to review everything, then provide no tools or time to do it. Human review matters, especially for critical paths, but memory is not a scalable control. Add automated checks for package resolution, lockfile changes, dependency policy, secrets, and known vulnerabilities. Reserve expert review for the choices that automation cannot judge well, such as whether an authentication flow fits the threat model.
Finally, do not assume that one source of grounding solves the problem. Documentation can be out of date, can describe a different version, or can be misread by the model. Check that the source applies to your software and then test the code. OWASP’s misinformation guidance is a reminder to verify claims rather than accept them because they sound authoritative. A useful review asks, What did we check? not, Did the assistant sound sure?
Frequently Asked Questions
What is an AI coding hallucination?
It is a plausible-looking but false code suggestion or technical claim produced by an AI model. Examples include an invented package, a function that is not in the library, or security advice that does not fit current guidance. The code may still look polished, so verify important claims independently.
What is slopsquatting?
Slopsquatting is a software supply-chain attack in which someone registers a package name that an AI coding tool has hallucinated. If a developer later installs that package, they may run code controlled by the attacker. Check the exact package in its official registry before installation.
How can a team reduce the risk of AI-generated dependencies?
Require registry verification and review for new packages, use an approved-source policy where practical, and inspect lockfile changes. Add CI checks that flag unresolved or unapproved dependencies. Restrict build permissions so that a package or script cannot reach secrets or systems it does not need.
Are passkeys safer than SMS codes for important accounts?
Passkeys can resist phishing and avoid reusable shared passwords, making them a stronger option for many important accounts. Recovery still needs careful design. NIST’s digital identity guidance treats text-message delivery as a restricted method, and the FIDO Alliance explains how passkeys work. Choose authentication based on the account’s risks and support needs.
AI coding tools can make useful work quicker. That does not make their output trustworthy by default. Keep the benefit, and put verification where it belongs: in the code review, dependency policy, build pipeline, and security checks. I would rather see a modest assistant suggestion pass through strong controls than a brilliant one go straight to production. The work of building software carries responsibility because real people depend on it; protecting their accounts, data, and time is part of doing the job well.