Alleged Deepfake Videos Impersonate Lithuanian Politicians and Doctors in Purported Cross-Border Scam Network

An alleged series of high-quality AI-generated deepfake videos reportedly mimicked Lithuanian TV segments, politicians, and doctors to promote fraudulent health products and anti-vaccine disinformation. Distributed mainly via Facebook, the videos reportedly reached audiences in up to 15 EU countries. Fact-checkers and cybersecurity experts reportedly described the campaign as part of a coordinated scam network designed to extract money by impersonating trusted public figures and media outlets.

Risk classification

• Primary risk domain: 4 Malicious actors
• Primary risk subdomain: 4.3 Fraud, scams, and targeted manipulation

The scammers used AI deepfake technology to impersonate trusted public figures and doctors in order to run a financial scam and steal money from victims.

Additional risk subdomains

• 4.1 Disinformation, surveillance, and influence at scale: The scam campaign also spread anti-vaccine disinformation at scale across multiple EU countries to attract attention and amplify their reach.

Causal factors

• Entity: Human
• Intent: Intentional
• Timing: Post-deployment

The incident was caused by human scammers intentionally using deployed AI deepfake generation tools to create fraudulent videos to steal money.

EU AI Act risk tier

• Risk tier: 3 Limited Risk

Limited Risk: The report describes the creation and dissemination of AI-generated deepfakes. Under the EU AI Act, deepfakes are classified as limited risk, requiring specific transparency obligations to inform users that the content is AI-generated.

AI system and alleged parties

• AI system: None named
• AI purpose: Deepfake Video Generation; Voice Generation
• Behaviour type: Tool
• Alleged developer: Unknown voice cloning technology developers, Unknown deepfake technology developers
• Alleged deployer: Unknown scammers, Unknown international scam network operating across multiple EU countries
• Alleged harmed parties: Vytautas Kasiulevičius, Social media users in Lithuania, General public of the European Union, General public of Lithuania, Aurelijus Veryga

Harm severity

Severity ratings are based on this scale.

Highest direct severity in any category: Minor. Severity is scored from Negligible to Catastrophic in each harm category, for harm the reports describe as caused directly or indirectly by the AI system.

• Physical: direct Negligible, indirect Negligible
• Infrastructure: direct Negligible, indirect Negligible
• Property: direct Negligible, indirect Negligible
• Financial: direct Minor, indirect Negligible
• Environmental: direct Negligible, indirect Negligible
• Malicious content: direct Minor, indirect Minor
• Differential treatment: direct Negligible, indirect Negligible
• Civil rights: direct Negligible, indirect Negligible
• Democracy: direct Negligible, indirect Negligible
• Privacy: direct Negligible, indirect Negligible
• Psychological: direct Negligible, indirect Negligible
• Epistemic: direct Minor, indirect Minor
• Child sexual exploitation and abuse: direct Negligible, indirect Negligible

Financial

Reported: The report explicitly describes financial losses from people being robbed through scam links, though the total amount is not quantified.

Directly caused: The report notes that 'more people were robbed' through scam links designed to steal money, but does not provide a specific monetary figure.

Inferred additional harm: Given that hundreds of thousands of people were exposed and 'more people were robbed' across 14-15 EU countries, total financial losses are likely in the hundreds of thousands of euros.

Malicious content

Reported: The report explicitly describes highly convincing fake news videos and deepfakes promoting fake medical products and spreading anti-vaccine disinformation.

Directly caused: Scammers created 20 high-quality deepfake videos mimicking Lithuanian TV news, featuring fabricated interviews with politicians and doctors.

Indirectly caused: The videos were shared and amplified by anti-vaccine social media groups on Facebook, reaching hundreds of thousands of users across 14-15 EU countries.

Inferred additional harm: It is likely that similar malicious deepfake videos continue to be generated and spread by the same international network across other European countries.

Epistemic

Reported: The report explicitly describes epistemic harm caused by the spread of anti-vaccine disinformation and fake medical claims using fabricated interviews.

Directly caused: The deepfake videos fabricated interviews with MEP Aurelijus Veryga and doctor Vytautas Kasiulevičius to falsely claim vaccines harm health and promote fake products.

Indirectly caused: The dissemination of these videos on Facebook misled viewers and polluted the information ecosystem across multiple EU countries.

Inferred additional harm: The high quality of the deepfakes likely contributed to a broader erosion of trust in digital media and public health information among the exposed population.

People affected

• Occurrences reported: 1
• People reportedly harmed: 1000
• People reportedly exposed: 200000

Potential causes

Management

• Inadequate Platform Moderation: Social media companies prioritized other areas over Baltic content review.

Technology

• High-Quality Deepfake Generation: AI tools generated highly realistic videos with flawless visuals.
• Improved Baltic Language Synthesis: AI systems synthesized Lithuanian speech with high grammatical accuracy.
• Ease of Access to AI Tools: Scammers easily accessed public AI tools requiring only basic prompts.

Data Inputs

• Publicly Available Footage Use: Scammers used public social network videos of politicians and doctors.

Human Factors

• Susceptibility to Sensationalism: Audiences fell for emotional narratives about vaccine harm.
• Lack of Critical Verification: Casual viewers failed to verify source domains or notice minor glitches.
• Amplification by Activists: Anti-vaccine groups willingly shared the fake videos, increasing reach.

Process and Methods

• Slow Platform Content Removal: Social media platforms failed to quickly detect and remove deepfakes.
• Ineffective Platform Contact: Agencies tried contacting Facebook but saw no immediate results.

Regulatory Environment

• Non-cooperative Jurisdictions: Perpetrators operate from Russia and Ukraine, hindering investigations.
• Lack of Cross-Border Enforcement: Lithuanian police lack jurisdiction to tackle international scam networks.

Information quality

• Classification confidence: High
• Reason for confidence: The reports from LRT and TVP World provide consistent, detailed accounts of the deepfake scam campaign, including the specific public figures targeted, the platforms used, the geographic reach, and the mechanisms of the fraud. The role of AI is explicitly stated, and there are no conflicting accounts.
• Ambiguities identified: The exact financial loss suffered by victims and the precise number of people who lost money are not quantified.
• Alternative interpretations: The incident could be viewed primarily as a public health disinformation campaign, but the explicit goal of stealing money through scam links strongly supports the classification of financial fraud.

An international scam network leveraged advanced AI deepfakes to impersonate Lithuanian political and medical figures, launching a multi-nation disinformation and financial fraud campaign across the EU. This incident represents a substantial information warfare challenge, showcasing evolved capabilities in localized synthetic media that complicate law enforcement and state response.

• Overall national security impact: Substantial
• Scope: Multiple nations
• Primary target: Lithuania
• Other affected: European Union countries
• Alleged perpetrator: International scam network

Threat characteristics

• Imminence: Long-term. Represents an ongoing strategic concern regarding the proliferation of localized, high-quality deepfakes by adversaries in uncooperative jurisdictions.
• Autonomy: Human-controlled. AI tools were utilized directly by human operators to generate specific video and audio content based on manual prompts.
• Novelty: Evolved capability. Demonstrates a significant evolution in the scale, quality, and linguistic targeting of deepfake operations reaching multiple sovereign nations.

Impact by dimension

• Physical security: Negligible. No physical threats, kinetic targeting, or critical infrastructure compromise reported in connection with this incident.
• Information security: Substantial. Coordinated campaign used high-quality deepfakes of political and medical figures to spread anti-vaccine disinformation at scale across 14-15 EU countries.
• Sovereignty: Minor. Impersonation of a Member of the European Parliament and former minister, representing a minor challenge to political communications but no disruption of core state functions.
• Economic security: Minor. Financial fraud targeting citizens across multiple EU nations, though lacking systemic impact on financial markets or critical technologies.
• Societal stability: Minor. Potential to erode public trust in health authorities and democratic institutions, but did not cause large-scale civil unrest or systemic rights violations.