Description: The Matryoshka disinformation campaign allegedly used AI to impersonate academics, reportedly spreading claims supporting Russia and urging Ukraine's surrender. These videos are said to have misrepresented scholars’ views in order to amplify pro-Russian propaganda. The campaign reportedly exploited global social media platforms to allegedly mislead viewers and undermine trust in credible academic voices while fueling misinformation about the Ukraine conflict.
Editor Notes: Reconstructing the reported timeline of events: (1) September 2023: The Matryoshka disinformation campaign reportedly began, allegedly producing anti-Ukrainian propaganda and distributing it via stolen accounts and bots on platforms like X and Telegram. (2) November 2023: Reports from Viginum reportedly revealed similarities between Matryoshka and Russia's GRU-linked Doppelgänger network, allegedly indicating potential shared origins. (3) Early 2024: The campaign allegedly began using AI to generate deepfake videos of academics from institutions like Cambridge and Harvard. (4) November 2024: The University of Bristol reportedly verified that a video falsely attributing political statements to historian Ronald Hutton was allegedly AI-manipulated. (5) December 13, 2024 (the date for this Incident ID): An investigation by The Insider reportedly confirmed that Matryoshka videos allegedly misrepresented academics’ voices to spread pro-Russian propaganda, with the footage heavily promoted in multiple languages by allegedly stolen social media accounts. When studying this incident, please also refer to Incidents 929, 931, 1060, and 1061. While Incident 931 has not been explicitly attributed to Operation Overload (also known as Matryoshka or Storm-1679), its thematic content and manipulation tactics appear adjacent. (No direct forensic or institutional linkage has been established as of this writing.)
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The OECD AI Incidents and Hazards Monitor (AIM) automatically collects and classifies AI-related incidents and hazards in real time from reputable news sources worldwide.
Entities
View all entitiesAlleged: Unknown deepfake technology developers developed an AI system deployed by Storm-1679 , Russian entities linked to GRU , Reliable Russian News , Operation Overload and Matryoshka, which harmed Ronald Hutton , Academics targeted by Storm-1679 , Academics targeted by Operation Overload and Academics targeted by Matryoshka.
Incident Stats
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
4.1. Disinformation, surveillance, and influence at scale
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- Malicious Actors & Misuse
Entity
Which, if any, entity is presented as the main cause of the risk
Human
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Intentional
Incident Reports
Reports Timeline
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The Russian disinformation network Matryoshka has launched a new campaign aimed at convincing social media users that scholars and professors from top global universities are calling for the West to lift sanctions against Russia. In the vid…
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The Russian disinformation network "Matryoshka" has launched a new campaign on the Bluesky social network. Eliot Higgins, founder of the investigative journalism group Bellingcat, has been one of the first researchers to detect its activity…
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The first symptoms of disinformation are emerging on the social media network Bluesky, with echoes of the pro-Russian “Matryoshka” campaign that flooded Elon Musk’s X — but with a few twists.
The @antibot4navalny collective, which specialis…
Variants
A "variant" is an AI incident similar to a known case—it has the same causes, harms, and AI system. Instead of listing it separately, we group it under the first reported incident. Unlike other incidents, variants do not need to have been reported outside the AIID. Learn more from the research paper.
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