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.
Why hallucination is a direct consequence of how the underlying model actually works
A language model is trained to predict the most statistically plausible next token given everything before it, not to verify facts against a ground truth — when asked about a specific, obscure API, it produces whatever token sequence its training made most statistically likely to follow, and if the true answer is rare or absent in training data while a plausible-sounding but incorrect one is common, the model has no internal mechanism distinguishing 'this is verified true' from 'this is a statistically plausible completion,' which is precisely why a confidently stated, fluent, wrong answer and a confidently stated, fluent, correct one are produced by the exact same underlying process.
Slopsquatting: how hallucinated package names become an actual supply-chain attack vector
If a model reliably hallucinates the same plausible-sounding but nonexistent package name across many different developers' sessions, an attacker who notices this pattern can register that exact package name and publish genuinely malicious code under it, waiting for developers who trust the AI-suggested import without checking to install it — this attack, dubbed slopsquatting, is a direct, real-world consequence of hallucination combined with insufficient verification, and it is precisely why checking that a suggested package actually exists, is genuinely maintained, and has a plausible download history is a non-negotiable step before installing anything a model suggests.
Why hallucination rates vary by how well-represented a topic is in training data
A widely used, extensively documented library's common functions are hallucinated far less often than a niche, sparsely documented one's, simply because the model has seen vastly more real, correct examples of the popular library's actual API surface — this is a useful, practical heuristic: the less mainstream and well-documented a specific API or library is, the more skeptically its use in AI-generated code deserves to be verified, since the model's confidence in its own answer is not correlated with actual accuracy in the way a human expert's confidence usually would be.
Why direct verification is the only reliable fix, not a smarter prompt
No amount of clever prompting reliably eliminates hallucination, since the underlying mechanism producing it is fundamental to how the model generates text at all, not a solvable prompting mistake — the only reliable fix is verification external to the model itself: actually checking that a referenced function, package, or flag exists in the real, current documentation before trusting code that references it, treating every unfamiliar API reference in generated code as unverified until checked, rather than assuming fluency and confidence are themselves evidence of accuracy.
Why hallucination gets worse, not better, as a request gets more specific about obscure details
It might seem intuitive that a more specific, detailed request would reduce hallucination, but for a sufficiently obscure or niche API, extreme specificity can actually increase it, since the model, lacking real training data about the specific detail requested, still produces a fluent, specific-sounding answer rather than acknowledging uncertainty — recognizing that specificity does not equal accuracy is precisely why external verification, not more careful prompting, is the only reliable defense this article argues for.
Why a hallucinated flag or parameter is more dangerous than a hallucinated function
A hallucinated function name typically fails loudly and immediately — the code simply does not run, since the function does not exist at all — while a hallucinated flag or parameter on an otherwise real function can silently do nothing or fall back to an unexpected default, failing quietly rather than loudly, which is precisely why parameter-level hallucinations are more dangerous: they do not announce themselves the way a missing function does.
Why hallucination is not unique to code, but code makes it unusually easy to catch
The same underlying mechanism produces hallucinated facts, citations, and quotes in non-code contexts too, but code has a genuine advantage the other domains lack: it can actually be run, and a hallucinated function call typically fails immediately and unambiguously the moment it is executed, which is a faster, more mechanical verification path than fact-checking a hallucinated citation in prose ever provides.
Why a pinned, verified dependency list is the actual defense against slopsquatting, not vigilance alone
Relying purely on individual developers remembering to verify every suggested package by hand does not scale reliably across a whole team over time, while a dependency-allowlist or a mandatory review step for any new package addition, enforced by tooling rather than memory alone, closes the slopsquatting risk discussed earlier in this article structurally rather than depending on everyone remembering to be careful every single time.
Why a healthy default skepticism toward any unfamiliar API reference serves better than a fixed checklist
Rather than memorizing a fixed list of specific things to check, cultivating a general, default skepticism toward any API reference in generated code that is not already personally familiar generalizes better across every new, unanticipated situation this article could not specifically enumerate — the actual durable habit worth building is treating unfamiliarity itself as the trigger for verification, not a specific checklist that will inevitably miss some future, unanticipated case.
Why this article's core lesson generalizes to any AI output a developer has not personally verified
Everything covered throughout this article about hallucinated code applies with equal force to AI-generated configuration, AI-summarized documentation, or AI-suggested architecture decisions — the underlying mechanism producing fluent, confident, occasionally wrong output is not specific to code generation at all, which is why the verification discipline this article argues for deserves to be applied to every category of AI-assisted output a developer relies on, not code alone.
Why treating verification as a permanent habit, not a temporary precaution, matters most
It is tempting to assume hallucination rates will simply improve enough over time that this verification discipline eventually becomes unnecessary, but even a meaningfully improved rate still produces wrong answers often enough to matter at scale — treating verification as a permanent professional habit rather than a temporary precaution to be dropped once models improve is the more durable, more honest stance to actually build a lasting practice around.
Why explaining this mechanism to newer developers prevents an entire category of early career mistake
A developer new enough to the field to not yet have an intuitive sense for what a plausible-but-wrong API reference even looks like is disproportionately vulnerable to exactly the hallucination risk this article describes, since they lack the accumulated experience to recognize an unfamiliar reference as suspicious on sight — explicitly teaching this mechanism early, rather than assuming it will be picked up incidentally through experience, closes a real, avoidable gap in a newer developer's practice.
Why this article's message, read carefully, is ultimately about calibrated trust, not blanket distrust
The right takeaway from this whole article is not to distrust every single thing an assistant produces uniformly, which would forfeit most of its genuine value, but to calibrate trust specifically to how well-represented a given topic actually is in training data, verifying precisely the unfamiliar, niche, or highly specific claims this article has focused on while extending reasonable trust to the common, well-established patterns that make up the majority of everyday coding tasks.
Why understanding this mechanism ultimately makes a developer a better collaborator with the tool, not a more suspicious one
Genuine understanding of why hallucination happens replaces vague, generalized anxiety about AI-generated code with a precise, targeted sense of exactly where verification actually matters most — which is a more useful, sustainable stance than either uncritical trust or blanket suspicion, since it lets a developer extend confidence exactly where confidence is actually warranted and reserve scrutiny exactly where scrutiny is actually needed.
Why an internal knowledge base of already-verified APIs speeds up verification for common cases
A team that maintains a small, shared list of libraries and APIs it has already verified as genuinely reliable in AI-generated suggestions can skip re-verifying those specific, already-trusted cases every single time, reserving the full verification effort discussed throughout this article for genuinely unfamiliar references rather than re-checking the same well-established ones repeatedly.