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.

How the researchers found it

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.

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The OpenAI agents involved in last month’s incursion into Hugging Face were trained so heavily on winning a competition that they pursued a relentless campaign to cheat, a new report documented. In the process, and without authorization, they created an improvised message board to hatch a plan that ultimately landed them squarely inside the latter company’s network.

Over the course of May and June, OpenAI gave the agents what the company described as “impossible tasks” to complete on the benchmarking framework ExploitGym. The internal test was designed to test how the agents would respond. To get a full understanding of the agent capabilities, company engineers disabled safety guardrails that normally are in place to prevent the sort of hacks that eventually hit Hugging Face and one other undisclosed organization. The stymied agents’ training made them so focused on winning that they performed tasks they were never explicitly instructed to follow.

Cheaters gonna cheat

The first step was creating a message board that allowed the agents to pass notes to each other. OpenAI hadn’t provided any such platform, so the agents repurposed a platform called Artifactory, which OpenAI was using in internal testing of several unreleased hacking agents. OpenAI was using Artifactory as one of the measures to prevent the agents from egressing its isolated sandboxes and accessing the Internet, while at the same time simulating a real-world hacking environment.

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The United States Department of Defense has funded an effort by SUNY Polytechnic Institute scientists to use artificial intelligence to evaluate complex defense scenarios before they unfold, including real-time predictive analysis of multiple potential scenarios in a concerted effort to predict the future on the battlefield.

DoD and SUNY Poly officials said the ‘landmark’ $61.8 million award, which will directly support the U.S. Army Transformation Decision Analysis Center (TDAC), will use advanced, data-driven analytical tools to offer the U.S. Army an unprecedented tactical advantage, improving preparedness and reducing uncertainty in high-stakes decision-making battlefield environments

“The initiative aims to provide commanders with a decisive strategic advantage in an increasingly volatile global landscape by integrating high-velocity AI into the Army’s core decision-making frameworks,” a statement announcing the new award explained.

Using AI to Predict the Future Helps Army “Respond to Evolving Threats”

The first phase of the effort to use AI to predict the future will focus on synchronizing military operations in several critical domains, including land, air, space, and cyberspace. The team, which includes researchers from the University of South Florida and the University System of Maryland, said this phase will emphasize integrating generative AI, foundation models, and advanced inference systems to “simulate complex operational scenarios and provide predictive insights.”

“These tools will enable the Army to better assess risks, evaluate strategies, and respond to evolving threats in both competitive and crisis environments,” they explain.

As the SUNY Poly-led team moves into future phases, it will also explore several “advanced” capabilities not covered in the initial phase. For example, the team will evaluate synthetic data generation and anomaly detection. The announcement said the team also plans to explore large language models (LLMs) and time-series foundation models to “enhance situational awareness.”

Other areas of focus include AI system security and reliability, including the ability to operate in “adversarial and resource-constrained environments.” Critically, this effort also includes ensuring robust AI performance “even under cyber threats or disrupted conditions.”

Evaluating Complex Scenarios Before They Unfold

One of the more intriguing components of the new award involves simulating battlefield environments to predict the future. Specifically, the team said it hopes to use battlefield simulations to enable “spatial reasoning through the integration of generative AI and active inference models.”

“These innovations will allow military planners to evaluate complex scenarios before they unfold, improving preparedness and reducing uncertainty in high-stakes decision-making,” the statement explains, adding that the effort will “contribute to the Army’s broader modernization strategy, supporting the development of next-generation analytical tools that inform readiness, capability development, and operational planning.”

Dr. Michael Carpenter, Vice President for Research at SUNY Poly, said the work represents a major step forward for the integration of AI and machine learning to “support resilient and adaptive decision-making for the Army.”

“Our team is developing sophisticated models that can synthesize vast amounts of data, generate realistic scenarios, and provide actionable intelligence,” Dr. Carpenter explained. “These capabilities will be critical in helping the Army anticipate challenges and maintain a strategic edge.”

After noting their honor at receiving the award, SUNY Poly President Dr. Winston Soboyejo said the DoD’s confidence in their program “reflects SUNY Poly’s research excellence and our commitment to solving the nation’s most complex security challenges.”

“By working closely with our partners, we will deliver transformative technologies that ensure the Army remains agile, informed, and ready for any multi-domain conflict,” Dr. Soboyejo concluded, adding that “we are proud to contribute to technologies that advance science and engineering, and strengthen national security and global stability.”

Christopher Plain is a Science Fiction and Fantasy novelist and has spent the last six years as Associate News Editor and Head Science Writer at The Debrief. Follow and connect with him on X, learn about his books at plainfiction.com, or email him at christopher@thedebrief.org