Do not install an AI-suggested package until you have independently verified its exact name, publisher, release history, security advisories, and provenance. If the package does not exist, remove the import and rewrite the code with a verified library or the standard library. If it exists but is vulnerable or suspicious, replace or upgrade it, review the complete dependency tree, run tests, and add a control so the same package cannot re-enter the project.
Generated code is untrusted input, just like code copied from an unknown website. A plausible package name may be a model error, a typo, an abandoned project, or a real package controlled by an unexpected publisher. Worse, an attacker can register a name that an AI invented and wait for developers to install it, a supply-chain technique OWASP calls slopsquatting.
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What a hallucinated dependency is—and why it is dangerous
A hallucinated dependency is a package name, version, import, module, or repository reference produced by an AI coding assistant that is not the real library the code assumes it is. The model may combine words from several genuine projects, invent a convincing name, confuse a package with an API, or cite a version that was never released. The resulting code can look complete while its dependency instruction is false.
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One large evaluation illustrates the scale without predicting every current model. Spracklen and colleagues analyzed 576,000 Python and JavaScript code samples from 16 models and reported that 440,445 of 2.23 million recommended packages—19.7%—were hallucinated, representing 205,474 unique nonexistent names. An earlier arXiv preprint from the same line of work reported averages of at least 5.2% for commercial models and 21.7% for open-source models in its evaluated cohort. These are results from a defined 2024 evaluation of model and registry snapshots, published as a USENIX Security 2025 paper; they are not a universal current rate for every language, model, or agent.
The safe response when an assistant names a package
- Pause installation. Do not copy an AI-generated
npm install,pip install,go get, or equivalent command into a shell. Remove it from any automated setup script until it passes review. - Record the exact request and output. Keep the package spelling, requested version, import path, repository URL, and the prompt that produced it. Small spelling differences can identify a typosquat or a different publisher.
- Verify the package in the official registry. Search the exact name and version. Confirm that the registry entry, linked source repository, publisher, maintainers, creation date, and release history all exist and describe the library the code needs.
- Check security intelligence. Query a relevant source such as the GitHub Advisory Database, OSV, or NVD. Inspect both the direct package and every resolved transitive dependency.
- Choose a known implementation. If the name is absent, mismatched, newly created without a credible reason, or unrelated to the requested API, delete the import and ask the model to rewrite the feature using a verified library or the standard library.
- Pin and review the result. Resolve the dependency in a controlled environment, commit the lockfile, inspect the resolved tree, run tests, and require a human review before merging.
OWASP’s NPM Security Cheat Sheet states: “Never blindly run npm install on packages suggested by AI tools without independent verification.” Its Secure Coding with AI Cheat Sheet likewise advises verifying every AI-suggested package on the public registry before installation.
How to verify that the package is real
Check exact identity, not a similar-looking result
Copy the package name character for character into the official ecosystem registry. Compare punctuation, scope, capitalization rules, and separators. For npm, distinguish a scoped package such as @scope/name from an unscoped package with a similar name. For Python, distinguish the distribution name searched on PyPI from the import name used in source code; they are not always identical. A search result that merely looks close is not confirmation.
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Confirm that the requested version is published and that its release date is plausible. If the assistant specified a version that the registry does not contain, stop rather than silently substituting the newest release. Record the version that was actually reviewed.
Confirm publisher and repository ownership
Read the registry’s publisher or maintainer information and follow its linked source repository. The account that publishes the package should have a credible relationship to the project. Compare the repository’s package manifest, release tags, documentation, and import path with the registry metadata. An unrelated repository, a copied description, or a publisher with no history for the claimed project is a reason to reject the package.
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Look at the creation date, release cadence, maintainers, issue activity, and changes between releases. A brand-new package created immediately after an AI began recommending an invented name deserves additional scrutiny. Newness alone is not proof of malware—some legitimate projects are new—but it should not bypass an organization’s minimum release-age policy.
Compare purpose with the generated import
Read the package documentation and source entry points. The stated API, supported runtimes, and examples must match the generated import and the feature you requested. If the code imports a function that the package does not expose, or the package solves a different problem despite a similar name, remove it. Ask the assistant to explain why the library is needed and to name an official API reference; treat its explanation as a hypothesis, not evidence.
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Downloads can provide context but are not a safety certificate. An established package with a consistent history is different from a package with a sudden, unexplained spike. Conversely, a small but legitimate internal or specialist project may have few downloads. Use download patterns alongside publisher identity, source history, advisories, and provenance rather than treating a threshold as proof.
Security checks before approving a dependency
Search advisories for direct and transitive packages
Query the GitHub Advisory Database, OSV, or NVD using the exact package name and version range. Read the affected-version boundaries, severity, vulnerable functions, and fixed versions. Then inspect the resolved dependency tree: a package can be clean while one of its transitive dependencies is affected. Save the advisory identifiers and the version you selected in the review record.
An advisory result of “no known issue” means only that no matching record was found at that time. New advisories can affect an unchanged lockfile, which is why scans should run on every pull request and on a schedule.
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Inspect install and build behavior
Review package manifests and lifecycle scripts before installation. Pay particular attention to code that invokes a shell, downloads another executable, reads credentials, accesses the network, deserializes untrusted data, or runs during installation or build. Generated code that adds such behavior needs explicit human approval even when the package itself is legitimate.
Evaluate provenance and integrity
Use signatures or provenance attestations where the ecosystem supports them. A provenance record can show where an artifact was built and how it relates to source control; it is not proof that the code is benign. Compare checksums or registry integrity data when available, and ensure the artifact came from the expected publisher and release pipeline.
Decision framework for an AI-suggested package
| Question | Acceptable evidence | Reject or escalate when |
|---|---|---|
| Does it exist? | The exact name and requested version appear in the official registry. | The name is absent, the version is missing, or only a similarly spelled result exists. |
| Who publishes it? | Registry publisher, source repository, maintainers, and release history align. | Ownership is unexpected, newly created without context, copied, or impossible to relate to the claimed project. |
| Is it maintained? | Releases, issue responses, documentation, and supported runtimes fit your requirements. | The project is abandoned, its releases are unexplained, or its API does not match the generated import. |
| Are vulnerabilities known? | Advisory sources show no applicable issue, or a documented fixed version is available. | A vulnerable version is selected, an exploit is relevant, or no compatible fix exists without a breaking change. |
| What is the reach? | The direct package and transitive tree are understood and intentionally approved. | Install scripts, deep transitive reach, or unexpected network and credential access are unexplained. |
| Can origin be verified? | Lockfile integrity, signatures, or provenance attestations match the expected release. | Artifacts cannot be tied to the reviewed source or publisher. |
| Can it be patched safely? | A secure release satisfies the API and tests pass. | The only fix requires an unreviewed API-breaking upgrade or feature rewrite. |
| When does the control run? | A pre-install or pull-request gate blocks unapproved packages, supplemented by scheduled scans. | The first check occurs only after vulnerable code has merged or shipped. |
Fixing a nonexistent or mismatched dependency
Delete the import instead of choosing a random replacement
If the exact package cannot be found in the official registry, remove its import and installation command. Do not install a similarly named package just to make the build pass. The absence may mean the assistant invented an API, so inspect the call sites and determine the actual requirement.
Rewrite with a verified library or the standard library
Ask the assistant to rewrite the smallest affected function using a package you have independently selected or a standard-library feature. State the approved package name and version in the prompt, and require the model to leave the code unchanged if the feature is already available in the standard library. Review the diff manually: replacing one hallucinated import can expose additional invented functions or configuration keys.
Test behavior, not just installation
Run unit, integration, and security tests that exercise the rewritten path, including malformed input and failure handling. A successful install proves only that a registry supplied an artifact; it does not prove that the implementation matches the requested behavior. Keep the lockfile and the review decision with the change.
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Fixing a real package with a known vulnerability
Use npm’s audit workflow for npm projects
- From the project root, run
npm auditand save the report with the commit or pull request. - Identify whether the finding is in a direct dependency or in a transitive package, and read the affected range and available fix.
- Review the proposed dependency-tree change before running
npm audit fix. npm warns that a fix can require code changes when an API changes; do not accept a major upgrade blindly. - If the change is compatible, apply it, update and commit the lockfile, run the project’s normal tests, and run
npm auditagain. - If no compatible fix exists, update the direct dependency manually, replace it with a maintained alternative, or remove the feature that requires it. Document the decision and any residual risk.
Run these checks in the project’s normal continuous-integration process as well as on a developer workstation. The audit output is a decision aid, not a substitute for reviewing code and the resolved tree.
Use Dependabot for GitHub repositories
Enable the repository’s dependency graph and Dependabot alerts. GitHub says Dependabot uses the GitHub Advisory Database and, where possible, opens a pull request for the minimum secure upgrade. Review the proposed files, lockfile changes, compatibility impact, and tests before merging; an automatically opened pull request still needs engineering approval.
When an alert identifies malware rather than an ordinary vulnerability, treat it as an incident. Do not simply upgrade and move on: isolate the dependency, inspect affected commits and build scripts, rotate credentials that may have been exposed, and restore from a known-good lockfile or commit.
Lock versions and the complete dependency tree
A package manifest expresses intent; the lockfile records the concrete artifacts that will be installed. Commit the lockfile and review changes to both direct and transitive entries. Pin approved versions according to your ecosystem’s normal practice, and make upgrades deliberate rather than allowing an assistant-generated command to resolve a moving target.
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Organizations can enforce an allowlist of approved packages and publishers, a minimum release-age policy, or both. A pre-install or CI gate should fail when a package is absent from the official registry, younger than the policy permits, outside the allowlist, or owned by an unexpected publisher. Exceptions should have an owner, an expiration date, and a written reason.
Build controls that prevent recurrence
Run checks before installation and before merge
The earliest useful control examines the proposed manifest before a package manager downloads code. Verify registry existence, publisher, age, and allowlist status, then inspect advisories and provenance. A second control runs on every pull request against the resolved tree. These stages catch different failures: the first blocks hallucinated or typosquatted names, while the second catches vulnerabilities introduced by a legitimate dependency or transitive update.
Scan continuously, not only when code changes
Schedule software-composition analysis and advisory checks even when the lockfile is unchanged. A newly published advisory can make an old artifact risky. Alert routing should identify the owning team, the affected service, the fixed version, and the deadline for remediation.
Review generated code according to its capabilities
Require human review for generated code that invokes shell commands, install scripts, credentials, network access, or deserialization. Review the dependency addition and the code that uses it together; a safe package can still be called unsafely, and a harmless-looking import can activate risky behavior through configuration.
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- Contain it. Stop builds and deployments that use the package. Disconnect or isolate affected development and build environments where practical.
- Preserve evidence. Record the package version, lockfile, registry metadata, install logs, generated files, and relevant commits before cleaning the environment.
- Inspect execution. Review lifecycle scripts, build steps, network connections, changed files, and access to environment variables or credentials.
- Rotate exposed secrets. Revoke and replace tokens, signing keys, passwords, and cloud credentials that may have been readable by the package or build.
- Restore a known-good state. Rebuild from a trusted lockfile or commit, remove the package, and verify the resulting artifact before resuming deployment.
- Escalate appropriately. Use your incident-response process, notify affected owners, and preserve the timeline and indicators needed for further investigation.
Limits of package and model signals
No single signal answers whether a dependency is safe. Registry presence does not establish benign behavior; popularity does not prove current maintenance; provenance does not prove that source code is harmless; and an advisory database can lag a newly discovered issue. Conversely, a new package is not automatically malicious. Make the decision from the combined evidence: identity, publisher, age, release history, purpose, advisories, transitive reach, integrity, and patchability.
Model-specific hallucination percentages should be used to justify verification controls, not to label a particular assistant. The published figures cover 16 models, Python and JavaScript samples, and registry snapshots used in a defined evaluation. Package registries, advisory records, model behavior, and repository security features change, so check the current package status and secure versions when implementing a fix.
Quick Recap
A practical review checklist
- Exact package name and requested version copied from the generated output.
- Presence confirmed in the official registry.
- Publisher, maintainers, repository, creation date, and release history checked.
- Package purpose and exported API match the generated import.
- Direct and transitive dependencies inspected.
- GitHub Advisory Database, OSV, or NVD checked for applicable issues.
- Install scripts, shell commands, credential use, network access, and deserialization reviewed.
- Signatures or provenance attestations validated where available.
- Known-good version selected, lockfile updated, and tree changes reviewed.
- Tests, audit checks, and composition analysis run before merge and on schedule.
- Human approval recorded for every new dependency and every generated high-risk operation.
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