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Trust Scam in Social Engineering |
In digital era, cybercriminals have discovered that easiest way to break into a system is not through code, but through human trust. One of most effective techniques in this domain is trust scam, a form of social engineering that manipulates individuals into believing attacker is a reliable partner, authority figure, or friend.
Unlike technical exploits, trust scams rely almost entirely on psychological manipulation supported by OSINT (Open Source Intelligence) to make deception more convincing. This makes them particularly dangerous, as even well trained professionals can be fooled.
What Is a Trust Scam?
A trust scam is a fraudulent scheme where attackers impersonate someone victim knows or should trust, in order to gain access to money, information, or systems.
How Social Engineering Drives Trust Scams
Social engineering tactics in trust scams include:
- Authority Bias: Pretending to be an official or expert.
- Reciprocity: Gaining favor by offering help or gifts before asking for something in return.
- Consistency: Gradually building trust over weeks or months before making demands.
- Exploiting Emotions: Leveraging love, fear, empathy, or urgency to reduce skepticism.
Attackers often use OSINT to strengthen their scams by:
- Gathering Personal Information - From social media, leaks, or public records.
- Tailoring Conversations - Mimicking hobbies, interests, or professional background of victim.
- Corporate Impersonation - Using LinkedIn or company websites to learn hierarchies and impersonate staff.
- Exploiting Leaked Data - Email addresses, passwords, or financial info from breaches make scams more credible.
Findings
- A 2022 Federal Trade Commission (FTC) report noted that romance scams alone caused $547 million in reported losses, a sharp increase from previous years.
- Academic studies show that trust scams are successful because victims often ignore red flags once trust is established.
- Forensic research indicates that many scams are coordinated by organized cybercrime groups rather than lone individuals.