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An AI coding agent confidently told a developer to npm install react-codeshift. Clean name, plausible purpose, exactly the sort of utility you'd expect to exist. It does not. Nobody ever published it. By January 2026, that non-existent package was referenced in 237 code projects, all of them AI-scaffolded, with agents still trying to fetch it daily.
That number is the whole problem in one line. A package that never shipped, invented out of nothing by a language model, propagated into hundreds of real repositories. The model recommended it consistently, and the humans trusted it. Charlie Eriksen at Aikido Security found react-codeshift in the wild and did the only responsible thing. He registered the name himself, before anyone with worse intentions could. That is the state of play. The safe move, right now, is for a researcher to squat the names your AI is about to invent, before an attacker does.
The attack class has a name: HalluSquatting. It turns an AI's confident wrong answers into a distribution channel, and it scales with agent adoption instead of despite it.
What Package Hallucination Actually Is
A large language model that writes code does not look anything up. It predicts the next token. When it emits import or require or a pip install line, it is producing the string that is statistically most likely to follow. It is not consulting npm or PyPI to check that the string points at something real. Most of the time the prediction lands on a package that exists, because real package names dominate the training data. Sometimes it lands on a name that is plausible, well-formed, and completely fictional.
The dangerous part is not that this happens. It is that it happens consistently. Give the same model the same kind of prompt and it will often invent the same fictional name, across different phrasings, different sessions, and different users. Researchers measured the consistency and it is not marginal. In the HalluSquatting study, invented names showed up in as much as 85% of a given resource request. For installable AI "skills," that figure hit 100%. The hallucination is not noise you can average away. It is a stable, predictable output you can profile.
Now add the agent. An AI coding agent does not just suggest the line. It runs it. It resolves the dependency, calls the package manager, installs, and in an auto-run configuration it imports and executes without a human reading the diff first. None of the tools tested in this research verified that a recommended package existed before recommending it. The registry check that a careful human does by instinct simply is not in the loop.
This is structurally different from typosquatting, and the difference is the point. Typosquatting targets human mistakes: you fat-finger expresss instead of express, and someone has parked malware on the typo. Human typos are a wide, shallow distribution of near-misses. HalluSquatting targets machine mistakes, and machine mistakes are narrow and deep. The model does not make a thousand different errors once each. It makes the same error a thousand times. An attacker only has to guess one name correctly to catch every developer whose agent shares that hallucination.
The Anatomy of the Attack
The mechanics are almost boring, which is what makes them work.
First, profile the target model. You do not need access to anyone's machine. You need API access to the assistant, or a copy of the open model, and a battery of realistic prompts. Ask it to scaffold a React refactor tool, a data pipeline, an auth flow, whatever your victims build. Record every package name it emits. Cross-reference against the real registry. The names that come back plausible-but-nonexistent, and come back repeatedly, are your candidates. The research team that formalized this showed the profiling reproduces across models and phrasings. The team: Aya Spira and Ben Nassi at Tel Aviv University, Stav Cohen at the Technion, Ron Bitton at Intuit.
Second, register the name. npm and PyPI let anyone publish anything under any unclaimed name, instantly, for free. Push a package that matches the hallucinated name, with a version number and a plausible README so a glancing human sees nothing wrong.
Third, host the payload and wait. This is the part that separates HalluSquatting from every attack that needs infrastructure. Once the name is registered, the attacker does nothing. No spam campaign, no phishing, no command-and-control beacon calling home. The AI does the distribution. Every developer who asks a profiled model the profiled kind of question gets handed the poisoned name, and every agent in auto-run mode installs it. The attack is passive after deployment. Its reach is a function of how many people use the model, which is a number going in one direction.
The researchers demonstrated code execution this way across nine AI coding assistants. The publicly named set: Cursor, Windsurf, GitHub Copilot, Cline, Google's Gemini CLI, and OpenClaw family assistants. Not a bug in one tool. A property of the category.
Why They Call It a Botnet
Traditional botnets are expensive to build. You need an exploit or a credential-stuffing campaign to get initial code onto machines, a propagation mechanism to spread, and a command-and-control layer to coordinate the fleet. Each of those is a place defenders can catch you: the exploit gets patched, the worm traffic trips an IDS, the C2 domain gets sinkholed.
HalluSquatting skips all of it. A single planted name, aggregated across many agent instances on many developer laptops and many CI runners, reaches all of them at once. The initial payload arrives not as network traffic to be inspected but as text the AI chose to emit. It passes straight through the firewall: it looks like every other package install the developer runs all day. There is no worm to detect because there is no propagation; the model is the propagation. There is no C2 needed for delivery because the model is the delivery. The researchers frame the result as an "agentic botnet," and the framing is fair. You get botnet-scale distribution with none of the botnet-scale infrastructure, and the fleet grows every time someone new adopts the tool.
The machines are not even a uniform target. Windows, macOS, Linux, a hardened CI container, a junior dev's laptop with admin rights: they all run the install the same way, because they all trust the same agent.
What --ignore-scripts Does Not Save You From
If you've been near the npm supply-chain conversation, your reflex defense is npm install --ignore-scripts. It disables lifecycle hooks so a package can't run a preinstall or postinstall script the moment it lands. I wrote up exactly that mechanism when a preinstall hook silently ran malware on npm install. Good habit. Wrong layer for this attack.
HalluSquatting does not need install hooks. The agent installs the package and then, because it recommended the package to use it, imports it. The payload fires at import time, when your code first pulls the module into memory. That is the same execution model I traced in the AsyncAPI CI compromise from the same week. No lifecycle script in the package; the malware wired to run on require(). You can install with scripts fully disabled, audit the package.json, find no hooks, and still get owned the first time the agent's own generated code executes import codeshift from 'react-codeshift'.
The gap is not at the install layer. It is at the recommendation layer. By the time the package name reaches your terminal, the mistake has already been made, upstream, by the model that invented it and the tool that surfaced it without checking. Every install-time defense is a lock on a door the attacker walked past.
The Tradeoffs, Honestly
There is one real, partial defense, and it is worth stating precisely because it is easy to oversell. A lockfile helps. If a project already has a package-lock.json, a reinstall resolves to the pinned set and a freshly hallucinated name cannot silently slip in on the next npm install. That closes the re-hallucination path for established projects, and it is a genuine reason to commit your lockfile.
It closes exactly nothing for greenfield work. A fresh project has no lockfile. Three greenfield patterns have no baseline to protect them: scaffolding from scratch, spinning up a prototype, and letting an agent build something new. That is precisely where AI coding agents are used most. The exposure is highest exactly where the tool is most useful. Presenting lockfiles as the answer would be telling you the seatbelt works, as long as you never drive a new car.
The fix that actually addresses the root cause is not a developer-hygiene tip. The fix is a tool-layer change. The assistant has to validate a package name against the live registry before surfacing the recommendation, refusing or flagging anything that doesn't resolve. None of the nine assistants tested did this. It is not a hard feature to build. A registry lookup is one HTTP request. The reason it isn't there yet is that "the model said so" has been good enough right up until someone started registering what the model says.
I run more than a dozen side projects through agent pipelines, and the honest reaction to this research was not "developers should check their dependencies." It was: which of my agents recommends a package name I'd install without looking? I run bloomii, a calm-news side project, on the same Claude Code pipeline that scaffolds everything else here. That pipeline can, and does, suggest npm packages. Until the recommendation layer verifies existence, the mitigation is embarrassingly manual. When an agent tells you to install something you don't recognize, check the registry first. Confirm the package is real, has a history, and matches what the agent described before you let anything run it.
Closing
The uncomfortable shift is that the vulnerability here is not a bug in your code or a weak password or an unpatched CVE. It is the model being confidently, consistently wrong, and an ecosystem that lets anyone claim the names it invents. react-codeshift is the documented example, not the last one. There is no confirmed mass-exploitation campaign on record yet; the packages found so far were claimed by a researcher getting there first. "Yet" is doing real work in that sentence.
The harness I run all of this on, agents and pipeline and Kanban board included, is open source: github.com/Ekioo/KittyClaw — MIT, star it if it's useful.
One question I don't have a clean answer to, and I don't think the tooling does either. Profiling which names a model hallucinates is something a defender can do just as well as an attacker. So why isn't every major registry pre-registering the top hallucinated names for the popular models and redirecting them to a warning, the way Eriksen did by hand for one package? If you've seen anyone doing this systematically, I want to hear about it.
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