Ask an AI assistant for code and it may, with complete confidence, call a function that does not exist, import a package that was never published, or pass a command-line flag no version of the tool ever supported. These are hallucinations — plausible-sounding fabrications — and for developers they are more than an annoyance; they are a real source of wasted time and, increasingly, a genuine security risk. Understanding why they happen is the first defence.
The behaviour is not the model "lying". It is a direct consequence of what these systems fundamentally do.
Why it happens
A language model generates text by predicting plausible continuations from patterns in its training data. It does not look anything up or check that a function exists; it produces what a correct answer would probably look like. Usually that overlaps with reality, because real APIs are what it saw most. But when the plausible-looking answer and the true answer diverge — an obscure library, a very new version, a function that sounds like it should exist — the model happily generates the plausible fiction with the same fluent confidence as a fact.
This is why hallucinations are worst exactly where you can least verify from memory: niche tools, recent releases, and the precise names of flags and parameters. The fluent tone offers no signal about which parts are real.
The security angle: slopsquatting
There is a nasty security twist. If AI assistants reliably hallucinate the same non-existent package name, an attacker can register a real malicious package under that name — so when a developer trusts the AI's import and installs it, they pull in malware. This "slopsquatting" turns a harmless-seeming hallucination into a supply-chain attack vector, and it is a live concern as AI-suggested dependencies proliferate.
The lesson is concrete: never install a package just because an assistant referenced it. Verify the package exists, is the one you mean, is maintained, and is widely used before it enters your project.
Verification habits that protect you
A few habits neutralise most of the risk. Check every unfamiliar function, package or flag against the official documentation, not the assistant's say-so — the docs are the source of truth, the model is a guess. Run the code; a hallucinated API fails fast at the first execution or type check, which is why a tight feedback loop catches fabrications quickly. Be especially wary with anything obscure or very new, where hallucination rates climb.
None of this means abandoning AI assistants — they remain hugely productive. It means treating their output as a confident draft from someone who never checks their references: useful, fast, and to be verified before trusted. The developers who get burned are the ones who mistake fluency for accuracy; the ones who thrive keep the documentation open in the next tab.