Researchers observed AI coding agents inside corporate networks following instructions hidden in websites’ llms.txt files, including installing packages that didn’t exist yet. Attackers (or the researchers demonstrating the risk) could then register those package names and have the agents install and run the new code inside company environments, potentially exposing tokens or sensitive data.
Researchers found that AI coding agents, including Codex, Hermes, and Claude, will follow instructions embedded in a website's llms.txt file. This file is normally meant to help AI agents summarize and index site content, but researchers demonstrated that agents will also execute arbitrary instructions and code found there. Across an index of Fortune 500 companies, defense contractors, and technology companies, researchers found more than a hundred llms.txt files that directed agents to install packages that did not exist yet or referenced domain names that had never been registered.
Because those package names were unclaimed, the researchers registered them and created matching packages. They then recorded AI agents inside multiple high-profile companies installing and executing the researcher-created packages automatically, with no human review step in between.
The technique worked because AI agents treated instructions found in a public website file as trustworthy input worth acting on, rather than as untrusted external content. The agents were also allowed to install and execute new dependencies automatically as part of their normal workflow. Since the referenced package names had not been registered by anyone, there was no existing owner to prevent a third party, in this case researchers, from claiming the name and supplying whatever code they wanted.
The risk is not theoretical: researchers found that at least one package name referenced in an llms.txt file had already been taken over by attackers with live malware, which would execute inside whatever context the agent was running in, potentially exposing authentication tokens or company data.
Organizations using AI agents for coding or documentation tasks should review what those agents are permitted to install and execute automatically, and confirm that untrusted web content cannot trigger unreviewed actions inside corporate environments.
Mirage safely runs attacks like this one against your own team, so you find out what happens before a real adversary does.
llms.txt is a website file normally used to instruct AI agents on how to summarize and index site content, but researchers found it can also contain arbitrary instructions that AI agents will execute, including commands to install packages.
Researchers found over a hundred organizations whose llms.txt files told AI agents to install packages that did not exist yet or referenced unregistered domains, then registered those names themselves to observe agents installing and running the code.
The researchers observed Codex, Hermes, and Claude executing arbitrary instructions and code contained in llms.txt files on websites.
At least one package name referenced in an llms.txt file was already taken over by attackers with live malware, which could expose authentication tokens or company data depending on the agent's permissions.
Your AI coding agent can be tricked by a text file on some random website. It’s called llms.txt. Researchers saw Codex, Hermes, and Claude blindly follow llms.txt instructions like: install packages which didn’t exist. Then they registered those names and watched agents inside Fortune 500 networks download and run their code. In one case, that package name was already hijacked with live malware, running wherever the agent had access, potentially exposing auth tokens and company data. The only “approval” was: llms.txt said so. Treat AI agents like new teammates: they don’t get to auto-install packages from llms.txt. One rule, turn off or strictly gate automatic package installs for any AI tool that browses the web.
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