Imagine asking your favorite AI tool to help you write code. It gives you a library name or a package that doesn’t actually exist. You rely on it, incorporate that code into your project, and proceed. But what if someone had created a malicious package with that fake name? Now, unknowingly, your system is compromised,, and the attacker didn’t need to do much at all. That’s the real-world risk caused by AI hallucinations in software dependency chains.
What Actually Happeneded
Between late 2023 and early 2025, cybersecurity researchers started noticing a strange new attack method. Developers using tools like ChatGPT or GitHub or Copilot were copying code that referenced nonexistentpackages. The AI models hallucinated these packages. Hackers noticed the trend and started registering those hallucinated package names on public repositories like PyPI and npm.
Once registered, those malicious packages started getting downloaded. Developers unknowingly introduced them to their applications. This new threat is now called slopsquatting, which is a form of typosquatting that leverages fake AI-generated code suggestions.
The vulnerability is not in the system but in the trust users place in AI outputs without verifying them. It’s an invisible and silent way for attackers to invade.
How AI Hallucination Vulnerabilities are Happened
Step 1– AI Hallucinates Code: AI models like ChatGPT, trained on billions of code snippets, sometimes create code or package names that look real but don’t actually exist.
Step 2– Developers Trust AI: Developers, especially under time pressure, copy this code without verifying it. They assume the AI has referenced an existing dependency.
Step 3– Hackers Register the Hallucinated Packages: Cybercriminals track these hallucinations by reverse engineering AI suggestions or simply scraping codes from forums. Then, they register those fake names that are now referenced on open-source repositories.
Step 4– Malicious Code Delivered: These malicious packages, once downloaded, can steal data. Open backdoors. Drop the other malware. Monitor your system for credentials.
Step 5– Attack Spreads Silently: Since no antivirus flags a package from a public repo right away,, the attack goes unnoticed for days or weeks. This isn’t ransomware. It is a slowow and stealthy supply chain poisoning.
Who Was Behind These Attacks??
There isn’t a single group like APT28 or Lazarus behind this. The conspiracy is broader. It is a technique,, not a campaign. We have linked these slopsquatting attacks to several individual attackers and criminal groups so far.
Cybersecurity firms like FOSSA, Phylum, and Trend Micro have tracked actors across GitHub, PyPI, and npm who monitor trending hallucinated names and automatically upload malicious code to match those.
It is very likely that state-sponsored actors will start adopting this method soon if they haven’t already. This type of attack costs very little to execute,, but the gain is enormous,, especially if it spreads through critical infrastructure or military vendor code.
Consequences and Financial impacts
The financial loss is hard to measure yet,, but the potential is massive. Let’s break it down.
Startups and Small Teams: They often don’t have proper dependency scanning tools. So they’re likely to include malicious packages unknowingly in production. Once exploited, recovery costs can go into tens of thousands of dollars.
Large Enterprises: These companies risk supply chain compromise across thousands of endpoints. Data theft,, internal tooling leaks,, or worse,, national infrastructure sabotage are possible.
Developers: Many individual coders may have their systems infected just from running test environments. This means that keyloggers, cryptominers, or full surveillance could occur without the coders’ knowledge.
National Security Risks: Since AI tools are now being used even in defense software development pipelines, this has become an international issue.magine AI suggesting a hallucinated module in the code that powers a government drone system.
The journalism community has started referring to the situation as the silent AI-led poisoning of the global codebase.
How to Protect Yourself ???
You’re not helpless. Here’s how to stay safe and reduce the risk.
Technical Defenses: Use dependency scanning tools like Snyk, OWASP Dependency Check, or GitHub Dependabot. Enable multi-factor authentication on repository accounts. Only install well-known packages and check the number of downloads and contributors. Manually inspect new dependencies introduced via AI. Block unknown or low-reputation packages in CI/CD pipelines.
Personal and Organizational Habits: Never blindly trust code generated by AI, and always search for and verify all packages before using them. Educate your team about the risks of slop squatting and the phenomenon of AI hallucinations. Set up alerts for unusual dependencies entering your system. Follow security mailing lists or GitHub threads to stay aware of trends. Regularly audit your current packages.
Study and Skill Building: Learn how to verify packages in PyPI, npm, and Maven. Study the structure of secure software supply chains. Understand how language models hallucinate by learning AI model limitations..
Shortly We Can Say
- AI hallucination is now an entry point for attackers.
- Developers must take responsibility for verifying the code before using it.
- Security teams need to update their threat models to include AI-generated risks.
- Public repositories must include safeguards against sudden name registrations.
Suggestions for Netizens:
Be curious. Don’t just copy and paste.
Be cautious. Always check what you’ve installed.
Be collaborative. Share newly discovered threats with your network.
What Does Hoplon Infosec Do?
At Hoplon Infosec, we assist businesses and developers in securing their software pipelines against emerging threats, particularly those related to AI vulnerabilities. We
- Audit your dependency chains
- Monitor public repositories for fake package trends.
- Educate teams about AI-integrated secure coding.
- Create custom alert systems for hallucinated dependencies.
In a world where AI can imagine the wrong thing, your first defense is knowing what’s real. We help you build that awareness one secure line of code at a time. This dilemma is not just about malicious code. It is about our growing overreliance on tools we barely understand.
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