Synthetic Trust Attacks: Modeling How Generative AI Manipulates Human Decisions in Social Engineering Fraud

Computer Science > Cryptography and Security [Submitted on 2 Apr 2026] Synthetic Trust Attacks: Modeling How Generative AI Manipulates Human Decisions in Social Engineering Fraud Muhammad Tahir Ashraf Imagine receiving a video call from your CFO, surrounded by colleagues, asking you to urgently authorise a confidential transfer. You comply. Every person on that call was fake, and you just lost $25 million. This is not a hypothetical. It happened in Hong Kong in January 2024, and it is becoming the template for a new generation of fraud. AI has not invented a new crime. It has industrialised an ancient one: the manufacture of trust. This paper proposes Synthetic Trust Attacks (STAs) as a formal threat category and introduces STAM, the Synthetic Trust Attack Model, an eight-stage operational framework covering the full attack chain from adversary reconnaissance through post-compliance leverage. The core argument is this: existing defenses target synthetic media detection, but the real attack surface is the victim's decision. When human deepfake detection accuracy sits at approximately 55.5%, barely above chance, and LLM scam agents achieve 46% compliance versus 18% for human operators while evading safety filters entirely, the perception layer has already failed. Defense must move to the decision layer. We present a five-category Trust-Cue Taxonomy, a reproducible 17-field Incident Coding Schema with a pilot-coded example, and four falsifiable hypotheses linking attack structure to compliance outcomes. The paper further operationalizes the author's practitioner-developed Calm, Check, Confirm protocol as a research-grade decision-layer defense. Synthetic credibility, not synthetic media, is the true attack surface of the AI fraud era. Comments: 15 pages, 3 figures, 2 tables Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.04951 [cs.CR]   (or arXiv:2604.04951v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.04951 Focus to learn more Submission history From: Muhammad Tahir Ashraf [view email] [v1] Thu, 2 Apr 2026 23:09:35 UTC (1,084 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)