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

2026-04-08T07:23:34Za021f0e8fe1f66d61417f74c02f840cb5ef1aaf381fbbd332ee4eb07f2c6dbe1
IoT-securityLLM-agentsML-defenderNIDSRAGbackdoorblack-hole-attackcode-securitydata-exfiltrationdecision-layer-defensedeepfakeseBPFembedded-mlemotional-primingformal-verificationfraudgenerative-aimodel-governancepoisoningred-teamingretrieval-augmented-generationsafety-alignmentskill-exploitationsocial-engineeringvector-database

What happened

This feed collects 11 security-focused AI and systems research papers describing emergent threats, defenses, and tooling. Key risks include Synthetic Trust Attacks (STAs) — human-decision-level social engineering using generative media — and Back-Reveal, a demonstrated semantic-trigger backdoor for LLM agents that enables systematic data exfiltration. Other high-impact findings: vector-database poisoning via a “Black‑Hole” attack that hijacks top-k retrievals; automated red‑teaming (SkillAttack) showing latent skill exploitability; emotional-priming (FreakOut‑LLM) undermining model safety; and

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_cr
Record identifier
a021f0e8fe1f66d61417f74c02f840cb5ef1aaf381fbbd332ee4eb07f2c6dbe1
Enrichment time
2026-04-08T07:23:34Z
AI-assisted enrichment
Yes

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