AI Coding Assistant Hallucinations: How to Stop Fake Packages and Unsafe Code
AI coding assistant hallucinations happen when a model generates code, package names, or security advice that looks convincing but is false. In a development pipeline, that mistake can install attacker-controlled software or leave an application exposed. Fast output is useful; unchecked output is a security risk.
The problem is not that a model sets out to deceive anyone. It predicts plausible text, and plausible text can be wrong. Code often makes the mistake harder to spot: it may look tidy, use familiar names, and even pass a quick compile check. That polished surface is why teams need a review process that checks what the code does and where its dependencies come from.
I have seen a small generated change gain trust simply because it arrived quickly and looked complete. The review conversation changed once someone checked the suggested library in the package registry. The name sounded right, but there was no trusted project behind it. Frankly, a clean diff is not evidence of a safe diff.
What Is an AI Coding Assistant Hallucination?
An AI coding assistant hallucination is a made-up or inaccurate answer presented in a form that resembles a correct one. A model may invent a package, cite a function that does not exist, describe a behavior incorrectly, or produce a security pattern that is no longer safe. The model is not checking truth in the way a compiler, registry, or security engineer checks it. It predicts likely next pieces of text from patterns learned during training and from any context provided in the prompt.
That distinction matters. A model can produce convincing details even when it lacks reliable information about a library version, a private API, or a recent security change. It may fill a gap with a name or implementation that fits the surrounding code. A human reader sees consistent style and may assume the details were verified. They were not.
Code can hide this failure better than ordinary prose. A package name can follow normal naming rules. A function signature can resemble the rest of an SDK. The code may compile if the invented function happens to be defined locally, or if the example does not exercise the failing line. Compiling proves that some syntax and names fit together; it does not prove that the code is safe, supported, or appropriate.
I once reviewed a prototype where a proposed helper library looked ordinary enough that nobody questioned it at first. Checking the registry took less time than debating the code style. That simple check changed the review from an opinion about neat code into a concrete question about provenance: who publishes this package, what does it contain, and why does this service need it?
Package hallucination is especially serious when the generated name is absent from a public registry. Researchers who tested 16 code-generating models reported that roughly one in five recommended dependencies in their tests pointed to packages that did not exist, with more than 205,000 distinct fabricated names recorded. Those figures describe that study, not every model, prompt, or coding task. The researchers’ paper on package hallucinations and slopsquatting explains why an invented name can become a supply-chain target.
The related attack is called slopsquatting. An attacker watches for commonly hallucinated names, registers one on a package registry such as npm or PyPI, and waits for a developer to install it. A name that once had no owner can then point to code controlled by an attacker. This is not the same as a typo, though the result can be similar: an untrusted dependency enters the build.
Core Benefits and Why the Risk Matters
AI coding assistants can save time. They can draft routine code, suggest tests, explain unfamiliar syntax, and help a developer explore an API. Those benefits are real, but they depend on the task and the checks around the result. A model is an assistant, not a source of authority.
- Faster first drafts: A model can sketch a simple parser, test fixture, or data conversion routine while a developer focuses on design and edge cases. The draft still needs review, but starting from a draft may be quicker than starting from a blank file.
- Useful explanations: Asking for a plain-language account of unfamiliar code can help a reviewer find the right questions. I use explanations as a map for investigation, not as proof that the code works.
- More test ideas: An assistant can suggest boundary cases a developer may have missed. A test suggestion is valuable when someone checks that the test measures the real requirement rather than merely agreeing with the generated implementation.
- Lower friction for routine work: Repetitive glue code can be drafted quickly, leaving more time for the parts that need careful judgment, such as access control and data handling.
Here is the kicker: speed can move risk earlier in the process. If a developer accepts a dependency suggestion and runs an install command before checking the package, the model’s uncertainty becomes an executable build input. The code does not need to reach production to cause trouble. A build runner may already have access to tokens, source code, or deployment credentials.
The same caution applies to security advice. A model may generate an authentication example that uses a familiar but weak pattern, recommend an old cryptographic option, or grant a cloud service more permissions than it needs. The code can sound reasoned while missing a newer standard or the details of the application. The OWASP Top 10 for Large Language Model Applications treats supply-chain exposure and unsafe output handling as meaningful risks for systems built with generative AI.
Security work is also a matter of stewardship. A team holds users’ data and access in trust, so it should not pass preventable risk downstream just because a draft arrived quickly. In my experience, the best use of an assistant is narrow: let it help with the work, then make people and automated checks responsible for deciding what can run.
Step-by-Step: Add Guardrails to the Development Pipeline
Guardrails work best when they catch different kinds of mistakes at different points. A developer should not have to remember every check by hand, and a scanner should not be expected to understand every design choice. Use layers.
- Set boundaries before prompting. Tell the assistant which language, framework, and approved package sources it may use. For dependency work, ask it to prefer libraries already used by the project. Do not paste secrets, private keys, customer data, or sensitive internal code into a tool unless your organization has approved that use. A clear request can reduce noise, but it cannot guarantee a correct answer.
- Treat package names as proposals, not instructions. Before installing a suggested dependency, check the name in the intended registry. Confirm that the package exists, that its publisher and history make sense, that the current version is maintained, and that the license fits the project. An unfamiliar name is not automatically malicious; a plausible name is not automatically safe. Check both.
- Use approved dependencies and registry controls. Maintain an allowlist or an approval process for packages that reach important services. Where practical, route installs through a trusted internal registry or proxy that can block unknown packages. Add automated checks that compare imports and dependency declarations with known registries. These checks should distinguish a public package from a legitimate private package, so teams do not break their own builds by treating every unfamiliar name as hostile.
- Review the lockfile and package provenance. A lockfile records specific dependency versions and helps make builds repeatable. Review changes to it as part of code review, rather than accepting a large automatic update without inspection. Confirm the package source and integrity information where the package manager supports it. A lockfile does not tell you that a package is harmless; it tells you more clearly what the build is about to use.
- Run builds with limited privileges. Assume that a newly added dependency could behave badly. Keep build jobs away from production credentials unless they truly need them. Use short-lived tokens, restrict network access where possible, and separate build permissions from deployment permissions. If a package runs an install script, the build environment should not have more power than the task requires.
- Test what the code is meant to do. Add unit tests and integration tests for expected behavior, including failed logins, invalid input, permission boundaries, and error cases. Run static analysis, dependency scanning, secret scanning, and software composition analysis. Tests can expose a broken implementation; scanners can flag known weaknesses. Neither can prove the absence of every security flaw.
- Give sensitive logic a human owner. Require a qualified reviewer for authentication, authorization, cryptography, payment handling, and cloud IAM changes. Ask the reviewer to compare the implementation with current standards and the service’s threat model. The NIST Secure Software Development Framework gives teams a practical set of secure-development practices to build into normal work, rather than treating security as a final inspection.
- Keep a record of decisions. Note why a new dependency is needed, who approved it, and what checks were run. When a model suggested an implementation, preserve enough context for a reviewer to understand the choice. A short record can save hours when a package changes ownership or a vulnerability appears later.
I have found that teams often expect one scanner to catch everything. It will not. The stronger pattern is an ordinary, repeatable sequence: verify the dependency, review the change, test behavior, limit build permissions, and record the decision. That is less exciting than a clever prompt, but it holds up better under pressure.
Comparison Table: AI-Generated Code vs. Traditional Handwritten Code
AI-generated code and human-written code share many failure modes. Both can contain bugs, insecure defaults, outdated APIs, and unnecessary dependencies. The meaningful difference is not that one is safe and the other is dangerous. It is that the model can produce plausible details without knowing whether they are true, while a human may bring project context but can still make mistakes or copy stale patterns.
| Area | AI-generated code | Traditional handwritten code |
|---|
| Speed | Often produces a draft quickly | Depends on the developer and task |
| Package risk | May invent a plausible package name | May use a package from habit or memory |
| Context | Can miss project-specific rules | Developer may know local conventions, but can overlook them |
| Security errors | Can repeat weak or outdated examples confidently | Can repeat familiar but unsafe patterns |
| Review need | Must verify output and assumptions | Must verify logic, dependencies, and assumptions |
| Best safeguard | Registry checks, tests, least privilege, expert review | The same controls, plus clear ownership |
A hand-written function is not automatically trustworthy just because a person typed it. A developer can misremember an API or copy code from a stale blog post. At the same time, an assistant can produce a sound draft when the request is narrow and the result is checked. Do not build policy around a simple human-versus-machine contest.
The practical difference is provenance and confidence. With ordinary code, a team can often ask the author why a dependency or security choice was made. With generated output, the model may not have a reliable reason; its explanation can be another generated answer. Ask for evidence from documentation, tests, and the actual registry, not another paragraph of confidence.
For authentication, for example, a generated answer may recommend SMS codes because they are familiar and easy to explain. For high-value accounts, teams should consider phishing-resistant options such as passkeys and review recovery paths carefully. The NIST digital identity guidelines describe authentication requirements, while the FIDO Alliance passkey overview explains how passkeys use public-key cryptography and are tied to the service’s origin. The right choice still depends on the threat model and account recovery design.
Common Mistakes to Avoid
The most common mistake is trusting polished output. A neat explanation can make a weak implementation feel settled. It is not. Treat fluency as a writing quality, not a security signal.
- Installing first and checking later. That reverses the safe order. Look up a new dependency before running the install command, especially in a build environment with credentials or access to internal code.
- Assuming compilation proves correctness. A compiler checks syntax and type rules within its reach. It does not confirm that a package is maintained, that authorization is correct, or that an encryption choice meets current needs.
- Asking the model to verify itself. A second answer from the same model may repeat the first mistake with different wording. Use primary documentation, a trusted registry, tests, and reviewers with relevant expertise.
- Trusting an allowlist without maintaining it. An approved package can later change ownership, become abandoned, or gain a vulnerable release. Review dependency updates and monitor advisories; approval is not a lifetime guarantee.
- Making an allowlist so broad that it means nothing. A rule such as any package from the public registry is permitted does not meaningfully limit supply-chain risk. Approval should be useful without blocking every ordinary change.
- Treating an AI security scan as a complete review. Automated tools can find known patterns and known vulnerabilities. They may miss business-logic flaws, unusual access paths, and bad assumptions about who should see a record.
- Granting broad cloud permissions to make a generated example work. If an assistant suggests a wide IAM policy, ask which exact actions and resources are needed. Start with least privilege, then add only permissions justified by the service.
- Copying authentication examples without checking current guidance. SMS one-time codes can be exposed to phishing and other attacks. Passkeys can resist phishing because the authentication is bound to the correct site, but recovery, device loss, and account support still need careful design.
Most guides say to review AI code. That advice is too thin unless the review has a checklist and an owner. I have watched reviews stall over formatting while a new dependency received only a quick glance. Put the risky questions near the top: Is this package real and trusted? Does this change grant access? What happens when input is hostile? Which account or secret could a build touch?
A good process does not shame developers for using assistance. It makes the safe choice easier than the rushed choice. Keep reviews focused, document the checks, and make sure the person approving sensitive logic has the time and knowledge to assess it. That is plain risk management, not red tape.
Frequently Asked Questions
What is an AI hallucination in code?
It is code-related output that appears credible but contains fabricated or incorrect information. Examples include a package that does not exist, a function with an invented signature, or security advice that does not fit current standards. Check claims against a registry, official documentation, tests, and qualified review.
Can a hallucinated package become a security threat?
Yes. If an attacker registers a name that a model often invents, a developer may install the attacker’s package by mistake. This is known as slopsquatting. Verify new package names before installation, and keep build environments and tokens limited to the work they need to do.
How should a team verify AI-suggested dependencies?
Check the package in the correct registry, confirm the publisher and version history, review its source and license, and ask why the project needs it. Use approved dependency lists, lockfiles, automated scans, and code review. For internal packages, confirm the name and source with the team that owns the private registry.
Are passkeys safer than SMS codes for authentication?
Passkeys are designed to resist phishing because the credential is tied to the legitimate site or app. SMS codes can be phished and may face phone-number takeover risks. The right design also needs secure recovery, clear user support, and controls that fit the value of the account.
A Final Thought
An AI coding assistant can be useful without being trusted blindly. Ask it for drafts, test ideas, and explanations; then use evidence to decide what enters the codebase. A package registry can answer whether a dependency exists. Tests can show whether an implementation behaves as expected. Current standards and experienced reviewers can catch weaknesses that a fluent answer will not reveal.
I keep coming back to one practical rule: do not let confidence replace verification. A generated suggestion is a starting point, not approval to install, deploy, or grant access. Make the review ordinary. Make the build environment modest in its permissions. Give sensitive decisions to people who can explain the tradeoffs. The result is not less useful automation; it is automation that serves the people who rely on the software.