This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
Foreign-operated networks, primarily based in Bangladesh and China, have utilized AI-generated imagery and text to manufacture the appearance of grassroots opposition to data centers in the United States. These campaigns leverage 'rural rage bait' and hyperlocal grievance content to deepen domestic political divisions and erode trust in U.S. infrastructure. The networks employ automated, low-identity accounts to seed this content across social media platforms, creating a false consensus that is then amplified by state-affiliated actors.
U.S.-state-themed Facebook pages reportedly operated from Bangladesh shared purportedly AI-generated imagery opposing data-center development. The pages allegedly used local-sounding names to push hyperlocal grievance content into U.S. communities, making offshore engagement farming appear like grassroots opposition.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Using AI systems to conduct large-scale disinformation campaigns, malicious surveillance, or targeted and sophisticated automated censorship and propaganda, with the aim of manipulating political processes, public opinion, and behavior.
Also classified as
The campaigns generated specific false stories, such as fake earthquakes in various states and fabricated tech news articles.
The incident involves coordinated, at-scale foreign influence and disinformation campaigns using AI-generated content to manipulate public opinion and political processes in the U.S.
Human
Due to a decision or action made by humans
Intentional
Due to an expected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The risk is caused by the intentional decisions and actions of human adversaries (foreign state actors and spam operators) using deployed AI systems to conduct influence operations.
Added categories peak at 4: Severe (Epistemic Harm, Inferred)
Foreign state and non-state actors from China, Russia, Iran, and Bangladesh deployed generative AI tools to conduct coordinated influence operations targeting U.S. critical data infrastructure and democratic processes. While direct engagement was limited, the incident highlights the evolving threat of AI-scaled disinformation designed to exploit domestic political divisions.
The AI systems, including ChatGPT and image generators, were used as tools by human operators to perform specific content generation tasks, producing text and images based on direct prompts.
Limited Risk: The report describes the use of chatbots (ChatGPT) and AI-generated content (images, videos, and articles) to influence public opinion, which falls under the category of AI-generated content requiring transparency obligations.
The AI systems were primarily used to generate social media posts, memes, and articles to populate inauthentic accounts and influence public opinion.
The reports from the New York Times, Alethea, and Conspirator0 provide highly detailed, consistent, and cross-verified accounts of the foreign influence campaigns, the platforms used, the specific themes of the AI-generated content, and the actors involved (Bangladesh spam network, Storm-1516, Rybar, and Chinese state media).
Some actors argue the opposition to data centers is entirely organic, though threat intelligence platforms documented clear coordination and inorganic elements.
Technology
Generative AI Abuse
ChatGPT used by foreign actors to create convincing fake articles and comics.
AI Image Generators
Used to mass-produce anti-data center slop and localized outrage memes.
Algorithmic Amplification
Social media algorithms prioritize high-engagement AI-generated content.
Data Inputs
Inauthentic Prompting
Operators feed manipulative prompts to generate targeted political propaganda.
Lack of Provenance Data
Absence of metadata to verify if social media images are AI-generated.
Misleading Localized Inputs
Prompts tailored with specific state names to mimic local grassroots voices.
Human Factors
Cognitive Vulnerability
Public susceptibility to emotionally charged, hyper-local AI-generated bait.
Domestic Political Exploitation
Local political figures adopting adversary AI aesthetics for campaign gains.
Credulity Toward Fake Media
Users trust fake TechCrunch/Gizmodo articles written by generative AI.
Process and Methods
Inadequate Bot Detection
Platforms fail to quickly detect and block AI-driven inauthentic networks.
Poor Content Provenance Tracking
Social networks lack systems to flag synthetic content at scale.
Delayed Attribution Processes
Threat intelligence identifies AI campaigns only after they achieve reach.
Management
Underfunded Trust and Safety
AI developers and social platforms underinvest in abuse prevention.
Prioritizing Engagement Over Truth
Platforms focus on user engagement, which favors sensational AI slop.
Inadequate Risk Assessment
Tech firms failed to anticipate LLM exploitation for infrastructure sabotage.
Regulatory Environment
Lack of Synthetic Media Laws
No strong regulatory framework to govern AI-generated disinformation.
Weak Infrastructure Protection
Regulatory gaps in protecting critical AI compute facilities from propaganda.
Unregulated Social Platforms
Insufficient legal pressure on platforms to clean up automated AI slop.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
