You accept an autocomplete suggestion. The function name looks perfect, the arguments make sense, the surrounding code compiles in your head. Then it fails at runtime, because the method does not exist. Your copilot did not make a typo. It invented an API out of thin air, described it with total confidence, and handed it to you as fact. That is an AI hallucination in code, and it is one of the most underestimated risks in AI-assisted development.
What an AI hallucination in code really is
A hallucination is fluent, plausible output that is not grounded in reality. Large language models predict likely sequences of text; they do not check their claims against a source of truth unless you force them to. In prose that produces a confident but fake citation. In code it produces something more insidious: a hallucinated API that looks exactly like the real thing.
The danger is that code hallucinations are camouflaged. A fabricated function follows your language's naming conventions, takes arguments that seem reasonable, and slots neatly into the surrounding lines. Nothing about it looks wrong until you run it, or worse, until it runs in a way you did not intend.
The most common forms of hallucinated code
AI coding errors from hallucination cluster into recognizable shapes:
- ▸Nonexistent methods on real objects, invented because a similarly named method exists on a different class.
- ▸Wrong function signatures, where the function is real but the parameters, order, or return type are fabricated.
- ▸Imaginary libraries and packages, complete with plausible install commands.
- ▸Fabricated API endpoints and response fields, especially for third-party services the model half-remembers.
- ▸Invented configuration options and environment variable names that were never part of the tool.
- ▸Made-up database functions or features that do not exist in your engine.
- ▸Version confusion, where the model blends behavior from several versions of a framework into one that never shipped.
Each of these compiles in the author's imagination and fails in reality.
Why models invent APIs and sound so sure
The confidence is the trap. Fluency and correctness are decoupled in a language model. It generates the most statistically likely continuation, and a well-formed, confident answer is more common in training data than a careful hedge. So the model produces authoritative prose describing a function that does not exist, in the same tone it uses for functions that do.
Several forces drive hallucination specifically in code:
- ▸Interpolation between patterns. If dozens of libraries expose a method with a certain name, the model expects yours to as well.
- ▸Mixed-version training data. Public code spans many versions of every framework, so the model blends them.
- ▸No execution grounding by default. Unless your assistant actually runs code or reads current documentation, it is recalling, not verifying.
- ▸Pressure to be helpful. A model tuned to answer will rarely tell you the function you want does not exist; it will invent one that would be convenient.
The hallucinations that survive review
The fabricated function that fails to compile is the easy case; your tools catch it in seconds. The dangerous hallucinations are the ones that run. A model may call a real function with subtly wrong assumptions about its behavior, believe an optional parameter defaults to a safe value when it does not, or mishandle an edge in an API that behaves differently across versions. The code compiles, passes a quick smoke test, and ships, and the defect only surfaces under a condition you did not test.
These survive precisely because they are plausible. They reflect how the API probably works, or how a similar API genuinely does work, which is convincing enough to pass a hurried review. The defense is the same discipline that catches everything else, real tests against real behavior and edge cases, but the lesson is that compiling is not correctness. A hallucination that runs is still a hallucination, and it is the one most likely to reach your users.
The supply-chain danger: slopsquatting
Here is where a nuisance becomes a genuine security problem. Models tend to hallucinate the same plausible package names repeatedly. Attackers have noticed. They register those hallucinated names on public registries and fill them with malicious code, a technique now called slopsquatting, a cousin of typosquatting. A developer copies the AI's suggested install command, pulls the package without a second thought, and now runs attacker-controlled code inside their build.
That turns a hallucinated dependency from a harmless failure, the package does not exist and the install errors out, into a live compromise, the package does exist because someone malicious created it. It is one of the clearest reasons never to install a dependency an AI suggested without first verifying that it exists and is reputable.
How to catch hallucinations before they ship
The good news is that hallucinations are catchable with discipline and tooling:
- ▸Run the code. The fastest way to expose a hallucinated API is execution. If it cannot run, it cannot hide.
- ▸Lean on the type checker and compiler. Strong typing catches a large share of fabricated methods and wrong signatures immediately.
- ▸Verify every dependency against the official registry: does it exist, who maintains it, how old is it, how widely is it used. Be suspicious of a package with a plausible name but almost no history.
- ▸Check APIs against official documentation, not against the model's description of them.
- ▸Pin dependencies with a lockfile and scan them with software composition analysis.
- ▸Prefer retrieval-grounded assistants that read current docs or run tests, over pure recall.
- ▸Never auto-install a suggested package inside your build without a human in the loop.
Building a workflow that assumes hallucination
The mature stance is to design your process as if every AI-provided external reference is unverified until proven otherwise. In practice that means treating generated code the way you would treat a pull request from a stranger: read it, question every unfamiliar call, confirm that libraries and endpoints are real, run it against tests, and require review before it merges. Add an allowlist for dependencies so new packages get a deliberate decision rather than a reflexive install. Wherever possible, use assistants that are grounded in execution or live documentation, because a model that can run the code is far less likely to hand you a fantasy.
None of this means abandoning AI assistance. It means refusing to let fluency substitute for verification.
The EU and accountability angle
For teams under EU regulation, hallucinated code is not just a bug, it is a governance issue. NIS2 places real weight on supply-chain security, which is exactly what slopsquatting attacks. Maintaining a software bill of materials and vetting dependencies moves from good hygiene to expected practice. And when confident, wrong code enters a regulated system, whether it handles personal data under GDPR or sits in critical infrastructure, the organization, not the model, carries the liability. Traceability of what you shipped and why becomes essential.
How TuniCyberLabs keeps AI-assisted code honest
We help teams get the speed of AI assistance without inheriting its fantasies. That means building the guardrails that catch hallucinations before production: type-safe pipelines, mandatory review gates, software composition analysis and dependency governance, a maintained software bill of materials, and a secure development lifecycle that treats every external reference as unverified until checked. Our nearshore engineers in Sousse bring senior scrutiny to AI-generated code at a cost that makes thorough review sustainable rather than a luxury.
If your team is shipping AI-assisted code and you are not certain what is real inside it, that uncertainty is worth resolving now. Get in touch with TuniCyberLabs for a review before a hallucinated API becomes a production incident.