Alleged Deepfake Video of Anthony Albanese Promotes Fake AUFIRST 'Tax Dividend' Trading Platform

Purported deepfake videos of Australian Prime Minister Anthony Albanese, featuring a synthetic voice with an American accent, were reportedly used in scam advertisements on YouTube in mid-2025. They reportedly claimed that Australians could earn thousands per month through an automated cryptocurrency trading platform called AUFIRST, framed as a government "tax dividend." Viewers were reportedly directed to a fraudulent site and urged to invest $400.

Risk classification

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

The incident involves the use of AI-generated deepfakes of a public figure to run fraudulent advertisement campaigns aimed at scamming individuals for financial gain.

Additional risk subdomains

• 3.1 False or misleading information: The scam ads generated and spread false information regarding government tax dividends and investment returns to mislead the public.

Causal factors

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

The incident was caused by human malicious actors intentionally deploying AI-generated deepfakes to defraud the public post-deployment.

EU AI Act risk tier

• Risk tier: 3 Limited Risk

Risk Level 3: Limited Risk. The report describes 'AI-generated content such as deepfakes' which pose a moderate risk and require transparency obligations under the EU AI Act.

AI system and alleged parties

• AI system: unspecified
• AI purpose: Deepfake Video Generation; Voice Generation
• Behaviour type: Tool
• Alleged developer: Unknown voice synthesis technology developer, Unknown deepfake technology developers
• Alleged deployer: Unknown scammers, Unknown AUFIRST platform scam operators
• Alleged harmed parties: Investors, General public of Australia, Australian investors, Anthony Albanese

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 Negligible, indirect Negligible
• Environmental: direct Negligible, indirect Negligible
• Malicious content: direct Minor, indirect Negligible
• 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 Negligible
• Child sexual exploitation and abuse: direct Negligible, indirect Negligible

Malicious content

Reported: Yes, the report describes malicious deepfake video and audio content created to impersonate public figures for fraudulent purposes.

Directly caused: The creation and distribution of deepfake videos of Anthony Albanese on YouTube promoting a fraudulent cryptocurrency scheme.

Inferred additional harm: It is likely that other similar malicious deepfake ads featuring other public figures were also generated and distributed across digital platforms.

Epistemic

Reported: Yes, the report describes the generation of false information, including a fictional transcript claiming the Prime Minister wanted to distribute tax dividends.

Directly caused: The fabrication of statements attributed to Prime Minister Anthony Albanese claiming he wanted to improve the tax system and distribute dividends.

Inferred additional harm: The proliferation of highly realistic deepfakes of political leaders can lead to a broader erosion of public trust in video evidence and official announcements.

People affected

• Occurrences reported: 1

Potential causes

Management

• Underinvestment in Review Controls: Failure to dedicate enough computing power and review staff to block scams.
• Reliance on Fully Automated Systems: Management relied on automation, failing to protect users from fraud.

Technology

• Advanced Deepfake Technology: AI allowed highly realistic cloning of voices and likenesses for scams.
• Automated Ad Delivery Systems: Fully automated systems allowed deepfake ads to bypass initial filters.
• Inadequate Detection Algorithms: Detection algorithms failed to flag the synthetic Albanese deepfake.

Data Inputs

• Misleading Video and Audio Inputs: Scammers used high-quality likeness and voice data of public figures.
• Fictional Transcript Data: Scam used fake transcripts promising high dividends to trick users.
• Unauthorized Likeness Data: Scammers exploited public audio and video to train AI clone models.

Human Factors

• User Trust in Public Figures: Targeted Australians trusted the deepfake of the Prime Minister.
• Sophisticated Scammer Tactics: Scammers exploited the growing trend of watching YouTube on TV screens.

Process and Methods

• Ineffective Ad Review Processes: Automated and human reviews failed to block the fraudulent deepfake ad.
• Flawed Ad Rejection Logic: Legitimate ads get rejected while harmful deepfakes slip through.

Regulatory Environment

• Lack of Immediate Platform Liability: Digital platforms face fewer immediate lawsuits than traditional TV stations.
• Absence of Finalised Codes: Government framework codes for digital platforms are still being finalised.

Information quality

• Classification confidence: High
• Reason for confidence: The report clearly describes the nature of the scam, the platform used, the target audience, and the specific public figures impersonated. There is little ambiguity regarding the malicious intent or the mechanism of the scam.
• Ambiguities identified: The exact number of victims and the total financial loss resulting specifically from the YouTube AUFIRST scam are not detailed.

Malicious actors utilized AI-generated deepfakes of Australian Prime Minister Anthony Albanese in fraudulent YouTube advertisements to promote a cryptocurrency scam. While representing a minor threat to information integrity and public trust in official figures, the incident is a localized, financially motivated fraud campaign with limited national security implications.

• Overall national security impact: Minor
• Scope: Single nation
• Primary target: Australia
• Alleged perpetrator: Unknown

Threat characteristics

• Imminence: Long-term. The threat represents an ongoing strategic concern regarding digital fraud rather than an immediate national security crisis.
• Autonomy: Human-controlled. AI tools were used statically to generate synthetic media assets, with humans controlling the creation and ad distribution processes.
• Novelty: Established threat. Deepfake impersonation of public figures for financial scams is an established threat vector that has been observed globally.

Impact by dimension

• Physical security: Negligible. The incident is a digital financial scam with no threat to physical systems, critical infrastructure, or human safety.
• Information security: Minor. While deepfakes of a political leader were deployed, the operation was a financially motivated scam rather than a state-sponsored information warfare campaign.
• Sovereignty: Minor. The unauthorized deepfake impersonation of a sitting Prime Minister could erode trust in official communications, but did not disrupt core government operations.
• Economic security: Minor. The scam targeted individual citizens for small-scale financial fraud, posing no systemic risk to national economic stability or strategic technology.
• Societal stability: Minor. The incident caused localized financial harm and minor epistemic confusion, but did not threaten broader social cohesion or civil liberties.