AI coding assistants can invent software packages, misuse real libraries, and produce insecure security logic. Their suggestions may look polished and even...
AI Hallucinations in Code Assistants: The Hidden Security Risks and How to Stop Them
AI coding assistants can invent software packages, misuse real libraries, and produce insecure security logic. Their suggestions may look polished and even pass a basic build, but fluency is not proof of correctness. Treat generated code as untrusted until your tests, dependency checks, documentation, and human review show that it is safe.
The concern is not that every suggestion is wrong. Most generated code can be useful, especially for routine tasks, but a plausible answer can slip past a rushed reviewer more easily than an obvious error. In my experience working with large engineering teams, review quality falls when people mistake readable code for verified code. A neat function can hide a bad dependency or a weak security choice. That is why AI assistance needs a place in the development process, not a free pass around it. The goal is not to ban a useful tool. It is to keep its speed while keeping responsibility for the software where it belongs: with the people and teams who ship it.
What Are AI Hallucinations in Code Assistants?
An AI hallucination is a confident-looking answer that contains information the model has made up or cannot support. In a coding tool, that might be a nonexistent package, a function that is not part of the library, or an explanation of a security control that does not match current guidance. The model may not be trying to deceive anyone. It is generating a likely continuation, not checking a fact against a live, trusted record.
Language models learn patterns from large collections of text and code. Given a prompt, they calculate which tokens are likely to come next. When the training examples do not cover a particular detail—or when the prompt leaves out important context—the model may fill the gap with a pattern that sounds right. It can also combine fragments from different library versions or similar tools. The resulting answer may be grammatically clean and technically wrong.
Code makes this problem harder to spot. Package names follow familiar naming styles. A fabricated function can look just like a real method in the same SDK. Even a successful compile proves only that some parts of the code fit together; it does not prove that the package is trustworthy, that the logic is secure, or that the behavior matches the user’s need. Tests help, but tests only check what someone thought to test.
One study of 16 code-generating models reported that roughly one in five recommended package names in its tests did not correspond to an existing package. The researchers counted more than 205,000 unique fabricated names across their test set. Those findings are useful evidence of a risk, not a promise that every assistant or every prompt will produce the same rate. Read the package-hallucination study and pay attention to its test setup before applying its figures to your own tools.
Here’s the kicker: a nonexistent dependency may stop being nonexistent. Security researchers describe “slopsquatting” as the practice of registering package names that AI tools commonly invent. If a developer installs one of those names, the package may run code under the permissions available to the build. I have seen how a routine dependency change can spread quickly through an application, which is why package review is not paperwork. It is a check on what code you choose to trust.
Core Benefits and Why the Risks Matter
There is a good reason developers use code assistants. They can draft repetitive tests, explain unfamiliar code, suggest a starting point for a small function, and help turn a rough requirement into a first pass. A useful assistant can save time on mechanical work and give a developer more room to think through design and behavior. The tool is valuable when a person remains in charge of the decision.
The security risk comes from the same speed. A suggestion can move from chat window to editor to dependency install before anyone asks whether its details are real. That is particularly serious in the parts of a system that control identity, private data, money, production access, or customer safety. A small mistake can have a much larger cost when it sits on a critical path.
The practical benefits of adding checks are straightforward:
- Catch fake dependencies early. Compare every new package with a trusted registry and your organization’s approved list. Lockfiles record exact versions, but they do not prove that a package is safe or appropriate.
- Find questionable code before merge. Static analysis, tests, secret scanning, and code review can each catch different problems. None is a substitute for the others.
- Protect sensitive systems. Require stronger review for authentication, authorization, cryptography, payment flows, and cloud access than for low-risk formatting code.
- Keep useful speed. When routine suggestions pass consistent checks, reviewers can focus their attention on the places where mistakes matter most.
- Make responsibility clear. A tool can propose an implementation; a team still decides whether it is fit to ship.
Frankly, claims that an assistant makes developers faster are not enough on their own. Measure how much rework, review time, and defect correction the tool creates as well as how many lines it drafts. I have found that a short, repeatable verification step beats a vague instruction to “be careful.” That approach respects the dignity of skilled work: people deserve tools that help them do their jobs well, not tools that quietly shift preventable risk onto them or their users. For wider secure-development practices, the NIST Secure Software Development Framework offers a useful foundation for building checks into the process.
Step-by-Step Guide to Reducing AI Coding Risks
A reliable process does not need to be grand. It needs to happen each time the assistant’s output could change software behavior or add a new trust relationship. Here is a practical sequence teams can adapt.
- Mark AI output as unverified. Treat generated code, commands, package names, and security explanations as suggestions, not facts. In code review, make the origin visible when it matters. This is not about blaming a developer for using a tool. It is about making sure reviewers know where assumptions may have entered the change.
- Check every dependency against the real registry. Search the official registry, then confirm the exact spelling, package owner, repository, release history, and documentation. Check whether the package is maintained and whether the claimed API exists in the version you intend to use. Be wary of a package with no credible history, a look-alike name, or a repository that does not match its registry entry. Do not install a suggested package just to see what it does on a machine with broad access.
- Use allowlists and lockfiles with care. An allowlist can stop unapproved names from entering a build, while a lockfile helps keep versions consistent across machines. Both are useful controls. Neither proves that a package is free of malicious code. Set a clear, reviewed exception process for new packages; otherwise, people will work around the rule when deadlines get tight. I have seen security controls fail not because they were unreasonable, but because the approved path was harder than the shortcut.
- Run builds with limited permissions. Give test and build jobs only the access they need. Keep production credentials out of ordinary development builds, use short-lived credentials where practical, and separate untrusted code from sensitive services. If a package turns out to be malicious, restricted permissions can reduce what it can touch. This is basic containment, and it matters whether code was written by a person or suggested by a model.
- Review security-sensitive logic against current guidance. For authentication, ask what threats the implementation handles, how recovery works, and whether the factor resists phishing. For cryptography, use well-maintained libraries and established protocols instead of inventing a design from a chat response. For cloud access, compare each permission with the task the service actually performs. The CISA guidance on phishing-resistant MFA and FIDO Alliance passkey information are better starting points than an unverified generated explanation.
- Test the behavior, not just the syntax. Write tests that cover expected outcomes and failure cases. Add checks for authorization boundaries, invalid input, expired credentials, and error handling where relevant. A program that compiles can still accept an unauthorized request or expose data. Ask what a malicious user could do, not only whether the happy path works.
- Ground answers in trusted documentation. If your assistant supports retrieval-augmented generation, connect it to documentation your team trusts and keep that material current. Grounding can reduce unsupported answers, but it cannot guarantee truth. Retrieved pages may be stale, incomplete, or unsafe, and a model can still misread them. Keep the source visible so a reviewer can check the actual passage.
- Track and improve the process. Record how often dependency suggestions are rejected, how frequently generated changes need correction, and what kinds of issues reviewers find. Use the numbers to improve prompts, approved tools, tests, and developer guidance. A benchmark from published work can help shape your own test set, but measure behavior in your repository and with your tasks. As the OWASP GenAI Security Project explains, misinformation is a recognized concern for large language model applications; teams should verify outputs rather than rely on confident wording.
The order matters. Check identity and permissions before trusting a package, then test the behavior before merging. I have watched teams spend hours debating whether AI code is “good” while missing the more useful question: what control would catch a bad answer automatically? Put those controls in the pipeline. Human attention is limited; repeatable checks are not.
Comparison: AI Coding Assistants vs. Traditional Autocomplete
AI coding assistants are often compared with traditional IDE autocomplete, but they do different jobs. Conventional autocomplete usually suggests symbols, arguments, or snippets based on the current file, project index, and editor rules. AI assistants can generate larger blocks, explain a design, and draw on patterns beyond the open file. That broader reach can help, but it also creates more room for invented details.
| Area | AI coding assistant | Traditional autocomplete |
|---|
| Typical output | Functions, explanations, tests, commands, or package suggestions | Symbols, method names, arguments, and local code completions |
| Main source of context | Prompt, surrounding code, and learned language patterns; some tools also retrieve documentation | Project files, language server data, and editor context |
| Common failure | Plausible but false API, unsafe logic, or invented dependency | Incorrect symbol suggestion or completion that does not fit intended behavior |
| Package risk | May recommend a name that is not a real package, or one that is a look-alike | Usually completes names already present in code or project metadata, though it can still mislead |
| Review need | Check facts, security assumptions, dependencies, and behavior | Check whether the completion matches the intended code and tests |
| Best use | First drafts, explanations, routine tasks with clear review | Fast local completion and known APIs already in the project |
This is not a claim that traditional autocomplete is safe by default. It can still complete the wrong method, and it cannot judge whether a developer’s design is secure. The difference is the breadth of what the AI assistant may invent. When I choose a tool for a task, I consider how much context it needs and what harm an incorrect suggestion could cause. A broad assistant can save time on a low-risk test fixture. The same tool deserves stricter checks when it proposes an authentication flow or a production permission policy.
Common Mistakes to Avoid
The most common mistake is treating polished language as evidence. A model can explain a wrong choice with a tidy paragraph, plausible terminology, and a confident tone. Read the source documentation. Confirm the version. Ask whether the answer fits your system rather than whether it sounds like something a senior engineer might say.
Another mistake is assuming that a successful build settles the question. Compilation checks only some properties of a program. It does not establish that a dependency is benign, that an access policy is narrow enough, or that authentication resists modern attacks. Tests can be weak or incomplete, too. Add tests that reflect real misuse and failure, and ask a reviewer with relevant experience to look at critical changes.
Teams also make a mess when they rely on one scanner. A dependency scanner may know about known vulnerabilities but miss a newly registered malicious package. A lockfile may pin a bad version precisely. Static analysis can miss flawed business logic. Each control has a boundary. Use layers, and understand what each layer can and cannot detect.
Security advice needs context, too. SMS codes remain in use and can be better than no second factor in some situations, but they are vulnerable to phishing and other attacks; do not treat them as the strongest option for high-value accounts. Passkeys can provide phishing resistance, but rollout, recovery, device access, and user support still require thoughtful design. The right decision depends on the threat model and the people who must use the system.
Finally, do not “fix” hallucinations by banning every assistant or by telling staff to be more careful. Those responses often move the activity out of sight without reducing the risk. I prefer a clear approved path, honest limits, and automated checks that do not depend on a reviewer spotting every oddity. Here’s the truth: people make mistakes, and fast tools increase the number of suggestions a team has to assess. Good process is not distrust of developers. It is stewardship of the systems and data they are responsible for protecting.
Frequently Asked Questions
Can AI coding assistants invent package names?
Yes. A model can generate a package name that looks normal but does not exist. An attacker may register such a name, creating a risk if a developer later installs it. Confirm every new dependency in the official registry, review its ownership and history, and use a controlled approval process. The published research on package hallucinations shows why this check deserves attention, but its reported rates should not be assumed to apply to every model or coding task.
Does code that compiles mean it is safe?
No. Compilation shows that code passes certain language and build checks. It does not prove that its logic is correct, that a package is trustworthy, or that permissions are appropriately limited. Use tests, dependency review, static analysis, security-focused review, and restricted build permissions. For security-sensitive code, compare the design with current standards and trusted documentation rather than accepting an assistant’s explanation as proof.
Are passkeys safer than SMS codes?
Passkeys are designed to resist phishing by tying authentication to the legitimate website or service. SMS codes can still be useful in some settings, but they can be intercepted or tricked out of a user and are not considered phishing-resistant. Teams should choose authentication methods based on the account’s risk, recovery needs, users, and available safeguards. For sensitive accounts, consult current CISA and NIST guidance rather than relying on an AI-generated recommendation.
Can retrieval-augmented generation prevent hallucinations?
It can help by giving a model trusted documents to use when it answers, but it cannot guarantee an accurate response. The documents may be old, the retrieval may return the wrong passage, or the model may misunderstand what it finds. Keep citations visible, check them against the original material, and maintain review for code that affects security or critical services. Grounding is one safeguard among several, not a replacement for verification.
Final Thought
AI coding assistants can make a developer’s first draft faster. They cannot take responsibility for the software that goes into production. That responsibility stays with the people who approve dependencies, design access, review changes, and decide when the evidence is good enough to ship. No tool’s confident tone changes that fact.
I have learned to be less impressed by a quick answer and more interested in the path that checks it. A verified package, a least-privilege build, a test that covers failure, and a reviewer who understands the security boundary are ordinary things. They are also the difference between useful assistance and an avoidable incident. Make the safe path the easy path, and developers can use these tools without pretending they are infallible.
Software is built by people, for people. Protecting the systems they depend on is part of doing the work well. Use AI where it helps, question it where the stakes rise, and put the checks in the pipeline before speed turns a guess into a release.