What Is Slopsquatting? AI's Fake-Package Security Risk Explained

Slopsquatting is what happens when an AI coding assistant recommends a software package that doesn't actually exist, and an attacker has already registered that exact name on a public repository and filled it with malware. A developer copies the AI's suggestion, runs the install command, and hands the attacker a way in. Nobody typo'd anything. It's a real, already-active supply chain risk, not a hypothetical one. Security researchers coined the term this year as AI coding tools became common enough that their hallucinations turned predictable – predictable enough for attackers to squat on the names before a developer even asks for them.

Why Do AI Coding Assistants Invent Package Names That Don't Exist?

Large language models predict what a plausible answer looks like, not what's actually true. Asked for a library to handle a specific task, a model will sometimes generate a name that follows every naming convention perfectly, correct casing, correct structure, a name that simply isn't registered anywhere. The model has no internal signal telling it the difference between a real package and a very convincing guess. It sounds equally confident either way.

How Are Hackers Exploiting This?

Attackers watch which fake names keep showing up across popular AI coding tools, then register those exact names on public package repositories before anyone else does. The package sits there looking legitimate, sometimes with a plausible README, until a developer's AI assistant recommends the same hallucinated name and the developer installs it without checking. From there the malicious code runs with whatever permissions that install script has, which on a lot of machines is plenty.

What Does It Actually Cost to Catch This Before It Ships?

Checking a dependency list against a real registry, by hand, before code ships takes a developer minutes. Hiring one  to do that kind of review work doesn't cost what people assume either. Freelancer's own numbers over the last 90 days put the average fixed-price web-development project at $151.53, and a full software-development build at $222.59, agreed upfront, before a single line changes hands. A dependency review sits well inside that band, not above it. Compare that to the actual cost of a supply-chain compromise, measured in incident-response hours and reputational damage, not a one-off invoice.

How Do You Check an AI Coding Suggestion Before You Ship It?

A few habits catch most of this. Search the exact package name on the real registry (PyPI, npm, crates.io, whichever applies) before installing anything an AI assistant suggests. Check the publish date and download count, a package that's three days old with twelve downloads isn't the same thing as an established library. Read the first few lines of an install script before trusting it with permissions. None of this replaces a second set of human eyes on anything shipping to production, it just narrows how much that person has to check. A dependency-review checklist is queued as a follow-up guide for exactly this.

Hire a Human Reviewer Instead of Hoping the AI Got It Right

None of this is an argument against using AI to code faster, plenty of freelancers doing this work use AI themselves and ship faster because of it. It's an argument for having someone in the loop who can tell a shortcut from a landmine. Hire a security-minded developer  to review dependencies before a release.