In the race to innovate, developers are increasingly leaning on AI coding assistants, a practice often called “vibe coding.” But this new frontier of software development has a dark side. We’re joined today by Maryanne Baines, a leading authority on cloud technology and software supply chain security, to unpack a new and subtle threat known as ‘slopsquatting’. We’ll explore how this attack exploits the very nature of AI, turning a developer’s greatest productivity tool into a potential Trojan horse. Our conversation will cover how automation can create the perfect storm for these attacks, the fascinating prospect of using AI to police other AIs, and the practical policies and security checks that can help organizations defend themselves in this new era.
You describe ‘slopsquatting’ as a modern twist on typosquatting, combining it with ‘AI slop’. Can you walk us through a real-world scenario of how an engineer using ‘vibe coding’ could be tricked, and what makes this threat more subtle than traditional typosquatting attacks?
Absolutely. Imagine an engineer, deep in the flow state, working on a tight deadline. They need a specific function, maybe to generate a unique type of chart. They ask their AI assistant, “suggest a lightweight Python library for plotting geospatial data.” The AI, in a moment of what we call hallucination, confidently suggests a package that sounds perfectly plausible, like py-geo-mapper. The name is logical, it fits the pattern of other libraries, but it doesn’t actually exist. Now, the attacker, who monitors these AI hallucinations, has already registered that exact name on a public repository like PyPI and uploaded malware. The engineer, trusting the AI’s output, simply types pip install py-geo-mapper without a second thought. The subtlety here is the source of the error. With traditional typosquatting, the mistake is human—you misspell ‘google’ or ‘requests’. Here, the suggestion comes from a trusted, authoritative-sounding tool, which completely disarms the developer’s natural skepticism. It’s not their typo; it’s a machine’s confident, but fabricated, recommendation.
A recent report found over 65% of engineers automate code reviews. How does this high level of automation, coupled with an AI’s ability to hallucinate non-existent packages, create a perfect storm for slopsquatting? Please detail the chain of events from AI suggestion to malicious package installation.
That statistic is the crux of the problem and it creates a terrifyingly efficient attack vector. It’s a chain reaction with very few human-centric off-ramps. The sequence goes like this: First, the developer gets the hallucinated package name from their AI assistant and adds it to their project’s requirements file. Second, they commit that code. This is where the automation kicks in. A Continuous Integration/Continuous Deployment (CI/CD) pipeline immediately picks up the new code. This triggers an automated review, but as the report shows, these checks are often configured to look for style-guide violations or syntax errors, not to vet the legitimacy of every new open-source dependency. The automated check passes. The next step is the build process, where the system automatically executes the command to install all dependencies. The malicious ‘slopsquatted’ package is downloaded and installed directly into the build environment, potentially compromising the entire system. It’s a perfect storm because the speed and trust we place in both AI and automation are exploited, creating a pathway for malware that bypasses the traditional, deliberative human oversight that I spent my entire career championing in the open-source world.
You propose a fascinating solution where AI assistants police each other. What specific algorithms or checks would one AI use to verify the suggestions of another? For instance, what steps would it take to validate a package’s legitimacy in a repository like PyPI before recommending it?
The concept of using AI as its own watchdog is incredibly promising. A “verifier” AI wouldn’t just take another AI’s suggestion at face value. It would act as an automated security analyst. When a coding AI suggests a new package, the verifier would initiate a series of checks. First, it would query the PyPI repository’s API to confirm the package even exists. If it does, it would immediately check its metadata. How old is it? A package created just a few hours or days ago is a major red flag. Next, it would look at the author. Is this a new author with no other contributions, or is it someone with a long, trusted history in the community? It could also analyze download patterns. A legitimate package usually sees gradual, organic growth in downloads. A slopsquatted package might have almost zero downloads for weeks and then a sudden, unnatural spike, which is a tell-tale sign of an attack being sprung. By codifying these checks into an algorithm, we can create a single, updatable model that provides a consistent, comprehensive layer of defense, far more scalable than relying on individual human reviewers to remember to perform these checks every single time.
Your firm implemented an AI usage policy as a “living document.” Beyond listing trusted tools, what are the core components of this policy? How does it specifically guide developers to avoid installing malicious packages and mitigate risks associated with shadow AI in their workflow?
A strong AI policy is so much more than just a list of approved applications; it has to be a framework for responsible innovation. One core component is a clear process for vetting and approving new AI tools. This directly combats “shadow AI,” where employees use unapproved tools that expose the company to risk. If an engineer wants to use a new AI assistant, there’s a defined security and operational review it must pass. Another key part is education. The policy must explicitly guide developers on how to interact with AI-generated code. We train them to treat every new suggested dependency with professional skepticism—to take 30 seconds to manually look up the package, check its GitHub repository, and look at its history before installing. Finally, the “living document” aspect is critical. The threat landscape changes weekly. Our policy is reviewed and updated quarterly to address new attack vectors like slopsquatting and to add new, vetted AI tools to the approved list. It’s about building a culture of security-conscious AI adoption, not just writing rules.
You compared sophisticated attacks to a museum heist, suggesting automated checks can make slopsquatting rare. What key metrics—like package age, author reputation, or download velocity—should these security tools prioritize to effectively distinguish a legitimate new package from a malicious, slopsquatted one?
That analogy really gets to the heart of it. We can’t stop every threat, but we can make the common attacks so difficult they become exceptionally rare, like a real-life museum heist. The automated security tools are our alarms and laser grids. To be effective, they must prioritize a few key metrics that build a “trust score.” First and foremost is
package age
. A package created within the last month should be flagged for mandatory human review. It’s just too new to have established a reputation. Second is
author reputation
. The tool should analyze the package author’s history. Do they have a track record of contributions to well-regarded projects? Or did their account just appear last week? A lack of history is a significant warning sign. Lastly,
download velocity and popularity
. The system should analyze download patterns. Is there a slow, steady increase from diverse sources, which suggests organic adoption? Or is there a sudden, massive spike from a narrow range of IP addresses, suggesting an automated campaign? By prioritizing and cross-referencing these metrics, the automated system can effectively filter out the vast majority of these low-effort slopsquatting attacks, leaving only the most sophisticated, “blockbuster movie” type of threats for our human experts to deal with.
What is your forecast for this new battleground between AI-driven attacks and AI-driven defenses?
My forecast is one of rapid escalation. We are at the very beginning of an arms race. Attackers will use AI to discover vulnerabilities, generate more convincing malicious code, and even automate the deployment of attacks like slopsquatting at a scale we’ve never seen before. In response, our defenses must become equally intelligent. We will see the rise of AI-powered security systems that don’t just rely on known signatures but can predict novel attack patterns, validate code provenance in real-time, and even automatically patch vulnerabilities moments after they’re discovered. The future of cybersecurity won’t be about humans versus machines; it will be about our AI fighting their AI. The winners will be the organizations that learn how to effectively partner with defensive AI, embedding it deeply into their development and security workflows to create a resilient, self-healing infrastructure. The human role will shift from being on the front lines to being the strategists who train and direct these AI defenders.
