Recently, a new method of attack has emerged that specifically targets the growing intersection between artificial intelligence and software development. This technique, known as “slopsquatting,” represents a clever exploitation of AI-assisted programming that could potentially bypass traditional security measures and insert malicious code directly into legitimate applications.

As AI becomes increasingly embedded in development workflows, understanding this vulnerability becomes essential not just for developers, but for anyone concerned with digital security.

What exactly is Slopsquatting?

The term “slopsquatting” was coined by security expert Seth Larson, combining two concepts already familiar in the tech world: “slop” and “squatting.”

“Slop” in the tech vernacular refers to low-quality, error-filled output produced by artificial intelligence systems. We’ve all seen examples of AI-generated images where subjects have six fingers or plastic-looking skin, or texts with factual inaccuracies presented confidently as truth. In programming contexts, “slop” describes code that functions but is inefficient, vulnerable, or unable to handle edge cases properly. It works, but just barely, and often harbors hidden flaws.

“Squatting,” on the other hand, has been a recognized practice in digital spaces for decades. It involves occupying a digital resource, like a domain name, social media handle, or package name, primarily to block legitimate owners or to deceive users. When someone registers domains with typos of popular websites (like “gooogle.com”), they’re engaging in “typosquatting,” hoping to catch users who mistype URLs.

Slopsquatting ingeniously combines these concepts. It involves cybercriminals anticipating the mistakes that AI coding assistants might make and preemptively registering package names that match these potential errors. When an AI assistant recommends or generates code that mistakenly references these malicious packages instead of the legitimate ones, the attacker’s code gets seamlessly incorporated into otherwise legitimate software.

How modern development practices enable Slopsquatting

The rise of slopsquatting as a viable attack vector stems from several converging trends in modern software development.

First, very few developers write code entirely from scratch. Instead, they use pre-packaged libraries and functions published in centralized repositories. These packages handle common tasks, saving developers from reinventing the wheel. When a developer needs functionality like processing JSON data or handling HTTP requests, they simply include a reference to an established package.

Second, AI coding assistants like GitHub Copilot, ChatGPT, and Cursor have become increasingly popular. These tools can generate substantial code segments, completing repetitive tasks and suggesting solutions based on patterns they’ve observed in millions of code repositories. Many developers now rely on these AI assistants to accelerate development, with some “prompt engineers” and “vibe coders” creating entire applications despite limited formal programming knowledge.

Third, these AI systems occasionally suffer from what researchers call “hallucinations”, instances where they generate plausible but incorrect information. In coding contexts, these hallucinations might manifest as references to packages that sound legitimate but don’t actually exist.

This is where slopsquatting finds its opportunity.

The mechanics of a Slopsquatting Attack

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A typical slopsquatting attack might unfold like this:

An attacker observes that when prompted to generate code for image processing, an AI assistant occasionally hallucinates a package called “image-procesor” (note the misspelling with one ‘s’) instead of the legitimate “image-processor” package.

The attacker creates and publishes a malicious package named “image-procesor” containing functional code that performs the expected image processing tasks but also includes hidden backdoors or data exfiltration capabilities.

A developer using an AI assistant receives code suggestions that include a reference to the non-existent “image-procesor” package. The code looks legitimate, and the package name seems plausible.

Taking the path of least resistance, especially if they’re relying heavily on the AI and may not fully understand all the code it’s generating, the developer incorporates this suggestion and installs the malicious package.

The resulting application works as expected, passing all functional tests, but secretly contains the attacker’s malicious code, which might collect sensitive information, create security backdoors, or cause other harm.

The brilliance (and danger) of this attack is that the malicious code isn’t injected after development or through social engineering. Instead, it’s unwittingly incorporated by the original developer during the creation process. The trojan horse isn’t smuggled in through the gates; it’s built directly into the city walls by the defenders themselves.

The research behind the threat

According to technical research published by a group of researchers from three American universities in March 2025, this threat isn’t merely theoretical. The researchers discovered that AI hallucinations in coding contexts follow systematic and predictable patterns, making it feasible for attackers to anticipate which package names might be erroneously generated.

The study revealed several concerning findings:

The predictability of these errors means attackers don’t need to use brute force to identify potential targets. They can simply observe AI behavior, identify commonly hallucinated package names, and register those.

About 38% of erroneously generated package names are semantically similar to legitimate packages, making them difficult for developers to spot as errors at a glance.

If a specific error becomes popular through repeated AI suggestions or unchecked tutorials, it creates a feedback loop where AI systems increasingly recommend the malicious package, amplifying the attack’s effectiveness.

Most worryingly, developers who rely entirely on AI assistance, particularly those with limited programming knowledge, tend to trust AI suggestions implicitly. If an AI includes a hallucinated package name that seems plausible, the path of least resistance is often to install it without further investigation.

Why traditional defenses fall short

What makes slopsquatting particularly insidious is how it bypasses traditional security measures. Most cybersecurity systems are designed to detect external threats, malicious downloads, suspicious network traffic, unauthorized access attempts. But slopsquatting exploits a blind spot: what happens when the threat is unknowingly built into the application by its legitimate creators?

It’s the digital equivalent of defeating an army not through frontal assault but by subtly tampering with their ammunition supply chain. By the time the application is deployed and security tools scan it, the malicious code is already integrated into what appears to be a legitimate component.

For companies that have reduced their reliance on skilled human developers in favor of AI-assisted development, often as a cost-cutting measure, this creates a particularly vulnerable situation. Without experienced eyes reviewing the AI-generated code, these subtle vulnerabilities can easily go unnoticed.

Strategies to mitigate Slopsquatting risks

The good news is that the security community is already developing countermeasures against slopsquatting attacks, even before they’ve become widespread in the wild.

The same researchers who documented the vulnerability found that certain AI models, particularly GPT-4 Turbo and DeepSeek, can identify incorrect package names that they themselves have generated with an accuracy exceeding 75%. This suggests that having AI review its own output for potential hallucinations could be an effective first-line defense.

Additionally, cybersecurity companies have developed specialized tools that integrate with development environments and browsers to detect potentially malicious packages before they’re incorporated into applications. These tools can flag suspicious package names or validate references against known legitimate packages.

Other recommended mitigation strategies include:

Human oversight: Having experienced developers review AI-generated code, particularly paying attention to package imports and dependencies.

Package lockdown: Using strict package management policies that only allow installation from approved sources or registries.

Dependency audits: Regularly scanning application dependencies for unusual or suspicious packages.

Supply chain security: Implementing software composition analysis (SCA) tools that validate the integrity and reputation of all included packages.

AI model selection: Choosing AI coding assistants that have been specifically trained to avoid package hallucinations or include built-in validation.

The Lesson Learned: AI assistance Is not AI replacement

Perhaps the most important takeaway from the slopsquatting vulnerability is what it teaches us about the proper role of AI in development and other professional contexts. While artificial intelligence offers tremendous capabilities for automation and assistance, it should complement rather than replace human expertise.

The companies most vulnerable to slopsquatting attacks are those that have eliminated experienced developers in favor of AI-only approaches, often in pursuit of short-term cost savings. What may look beneficial on a quarterly financial report can introduce subtle security vulnerabilities that are difficult to detect until it’s too late.

This pattern extends beyond software development. In any field where AI is being rapidly adopted, maintaining human expertise remains crucial, not just for quality control, but for security and risk management as well. The most effective approach combines AI efficiency with human oversight, creating systems that leverage the strengths of both while mitigating their respective weaknesses.

For now, security researchers appear to be ahead of malicious actors in identifying and addressing the slopsquatting vulnerability. There are no widely reported cases of successful attacks using this method in the wild yet. But cybersecurity is always a cat-and-mouse game, and new attack vectors emerge constantly.

The slopsquatting threat serves as an important reminder that new technologies often create new vulnerabilities, sometimes in ways that aren’t immediately obvious. As AI becomes increasingly integrated into our digital infrastructure, we must remain vigilant about its potential security implications.

By understanding threats like slopsquatting and implementing appropriate safeguards, organizations can continue to benefit from AI assistance in software development while maintaining robust security practices. The future of secure development lies not in choosing between human expertise and AI assistance, but in finding the optimal balance between them, leveraging the efficiency of machines while preserving the irreplaceable judgment of experienced human professionals.

After all, in the ongoing battle between digital guards and thieves, the best defense combines the strengths of both human intuition and artificial intelligence.