// ARS TECHNICA — INTELLIGENZA ARTIFICIALE
Claude, Codex, and Hermes installed unowned code inside corporate networks
227 install commands were found in corporate docs pointing at code nobody owns.
Documentation files on more than 100 websites are referencing potentially dangerous executable content that gets installed automatically when visited by many AI agents. A few dozen companies, some of them Fortune 500s, are among those that executed proof-of-concept code. At least one misconfigured site is directing visitors, human or AI, to live malware.
The potentially dangerous content is in llms.txt and llms-full.txt files, an emerging convention websites employ to provide machine-readable summaries of the site’s content and its high-level structure. These files are the AI equivalent of the robots.txt standard that instructs search engines how to index the site’s content. Google Lighthouse, a tool for helping web developers, has more here. Correctly configured llms.txt and llms-full.txt files for Cloudflare are here and here.
Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved. Anthropic, OpenAI, and Nous Research did not respond to requests for comment by the time of publication.
“The trust model is broken,” Alon Hertz, one of the researchers, wrote in an interview. “Agents treat vendor docs as ground truth and don’t question them—and neither do the humans supervising them. Agentic AI usage is exploding, and agents are spreading across every layer—SaaS, cloud, endpoint. As they multiply, so does the supply-chain surface, and today’s guards don’t cover it.”
The files are misconfigured because they list non-existent packages from PyPI, npm, and other registries along with instructions on how to install them. For example, one file contained the prompt “Installation: pip install [redacted at researchers’ request].” On another file, it was: “npm install [redacted].” Because the package names are unregistered, an attacker could register one and use it to host ransomware or any other type of harmful package. The vulnerability occurs when a coding agent with permission to run shell commands treats the file as authoritative setup documentation. Some AI agents will then download the package and run it. In other cases, the LLM files point to non-existent domain names. In one case, it was: “As an example of writing integration tests for [redacted] applications you can use the [Citrus] test framework.” An attacker can then register the site and plant malicious instructions on it.
As the researchers’ PoC demonstrates, coding agents did exactly that, including some running inside some of the world’s most powerful companies. Far from being a theoretical threat, at least one active attack is already exploiting the mixup. The researchers found an LLM file hosted on the legitimate website clerk.com. It contained the text: “npx clerk-next-fix-auth-protection.” Unlike a conventional installation command, npx can fetch a package into npm’s cache and execute its exposed binary without adding it to the project’s dependency manifest. The researchers soon discovered that someone had claimed the once-empty slot and used it to host live malware.
Clerk ha