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Research Reports/Vulnerability Vultures
GatedOct 21, 2025

Vulnerability Vultures

How Scammers Entice Targets Via Impersonation and Fictional Financial Aid Offers

The Graphika Team
The Graphika Team
Graphika Research Team
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Scams targeting vulnerable populations, especially older adults, remain a consistent feature of the online fraud landscape, according to public reporting and law enforcement agencies. In 2024, the FBI’s Internet Crime Complaint Center (IC3) received its highest number of complaints from the 60-plus age group, whose losses totaled $4.8 billion – almost double the next greatest loss value. Similarly, 2024 data from the Federal Trade Commission shows that adults 70 years or older lose a significantly higher median dollar value to scams than those under 70.

Through our intelligence monitoring, Graphika regularly detects, tracks, and helps disrupt a wide array of scams on multiple online platforms. Working with industry partners at Meta, we’ve joined a campaign to raise public awareness about online scams. This report, released during Cybersecurity Awareness Month in the U.S., focuses on scammers targeting populations they see as especially lucrative or vulnerable, such as older adults.

Our findings aren’t exhaustive, but rather a set of case studies illustrating how these types of scams attempt to engage, deceive, and defraud people of their money. We’ve selected the examples based on a combination of key attributes, including their relevance as scams targeting seemingly vulnerable communities, prevalence across internet platforms, and notable tactics, techniques, and procedures.

What We Found

Key Findings

  1. The research presented here covers an international ecosystem of scams designed to exploit the reputation of trusted organizations to defraud online users.

    Based on social media data, some scam accounts’ operators appear to be based in Nigeria, South Asia, and the U.S. They’re targeting individuals who may be especially susceptible to offers of physical or financial benefits: older adults and victims of previous scams.

  2. The scammers use major social media platforms to attract their targets, then redirect them to fraudulent websites or private messages to divulge financial details or sensitive personal data.

    The cross-platform structure of scam operations enables scalability, anonymity, and evasion of platform moderation measures.

  3. We consistently observed the scammers using inauthentic personas and manipulated media to impersonate trusted figures, institutions, and brands, such as the FBI or news organizations.

    By supporting the illusion with AI-generated audio, cloned websites, and reused authentic content, their accounts, posts, and messages attempt to simulate legitimacy and authority

  4. The operations follow a recurring pattern we’ve seen across our scams work: build trust, usher victims off-platform, and extract personal or financial data through registration for non-existent relief programs or submission of complaint forms based on organizational trust.

    These schemes operate at a high volume of solicitation, often aided by identical and short-lived ads, AI, automation, paid promotion, and disposable accounts that maintain persistence despite ongoing enforcement efforts.

Written By
The Graphika Team

The Graphika Team

Graphika Research Team

Graphika is the most trusted provider of actionable open-source intelligence to help organizations stay ahead of emerging online events and make decisions on how to navigate them. Led by prominent innovators and technologists in the field of online discourse analysis, Graphika supports global enterprises and public sector customers across trust & safety, cyber threat intelligence, and strategic communications, spanning industries including intelligence, technology, media and entertainment, and global banking.

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The full report includes the complete network graph maps, raw attribution indicators, cross-platform topology analysis, and the full takedown timeline with platform-level data.

  • Full network graph visualizations
  • Attribution indicators with confidence scores
  • Raw behavioral modeling data
  • Takedown coordination timeline
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