AI-Hallucinated Code Dependencies: The Emerging Software Supply Chain Risk

As generative AI tools increasingly integrate into software development workflows, their ability to generate code snippets, entire modules, and even infrastructure configurations has accelerated productivity. However, a troubling trend is emerging: AI hallucination of code dependencies, where AI generates non-existent or unreliable packages, APIs, or libraries, introducing a new and insidious software supply chain risk. This not only leads to development slowdowns and debugging nightmares but also opens a gateway for malicious actors to exploit hallucinated dependencies, potentially leading to malware infections, data leaks, or system compromises.

What Are AI-Hallucinated Dependencies?

AI hallucination occurs when a language model, such as GitHub Copilot or ChatGPT, produces confident but factually incorrect or non-existent information. In coding contexts, this can manifest as:

• Suggesting package names that do not exist
• Calling functions or methods that aren't part of any official API
• Recommending outdated, deprecated, or vulnerable dependencies
• Linking to GitHub repositories or modules that have never existed

For instance, an AI might suggest importing text-analyzer-helper from npm, even though no such package exists.

How Hackers Can Exploit This

If an attacker discovers that ChatGPT has recommended a non-existent package, they can quickly publish a malicious package under that name. The next time a user asks a similar question, ChatGPT might suggest the attacker’s now-real, but harmful, package. This tactic exploits AI-generated hallucinations, allowing attackers to distribute malicious code without relying on traditional methods like typosquatting or impersonating legitimate libraries. As a result, harmful code can end up in real applications or trusted repositories, posing a serious threat to the software supply chain. A developer seeking help from a tool like ChatGPT could unknowingly install a malicious library simply because the AI assumed it existed, and the attacker made that assumption true. Some attackers might even create functional, Trojan-like libraries that appear legitimate, increasing the chances they’ll be widely adopted before their true nature is uncovered. Once installed, these malicious packages can carry out a range of harmful actions. They may:

• Steal environment variables or sensitive credentials
• Open backdoors to allow unauthorized access
• Silently log and exfiltrate data

These capabilities make them particularly dangerous, as they can compromise both the security of the application and the integrity of the broader software supply chain.

Conclusion

AI-hallucinated code dependencies represent a new and deeply concerning frontier in software supply chain risks. As developers increasingly turn to generative AI tools like ChatGPT for coding assistance, the potential for attackers to exploit fabricated package recommendations becomes a serious threat. Developers, organizations, and AI providers must implement safeguards that verify and vet recommended packages before potential vulnerabilities become real-world compromises.