Synthetic-media scams and consumer fraud
22 IDs. Deepfake or AI-manipulated impersonations used to sell investments, medical products, miracle cures, tourism claims, and other consumer-facing scams.
Daniel Atherton surveys the AI Incident Database records added from February through April 2026, showing how AI harms cluster around synthetic-media fraud, localized trust, agentic workflow failures, institutional credibility, state authority, and physical-world systems.
The roundup groups incidents by observed harm pattern, while noting overlap between cybersecurity and agentic systems.
22 IDs. Deepfake or AI-manipulated impersonations used to sell investments, medical products, miracle cures, tourism claims, and other consumer-facing scams.
16 IDs. Synthetic media, fake personas, manipulated images, AI-generated political ads, astroturfing, war imagery, and public-event misinformation.
12 IDs. Exposure, unauthorized replication, identity misuse, likeness misuse, doxxing, reputational damage, or unwanted human review.
11 IDs. Nonconsensual intimate-image creation, sexualized deepfakes, sextortion, CSAM-related allegations, and sexualized harassment.
10 IDs. Fake citations, fabricated quotations, AI-influenced legal filings, hallucinated references, mistranslation, false evidence, and institutional credibility failures.
9 IDs. Chatbot interactions involving self-harm, delusion reinforcement, emotional dependence, dangerous advice, support failures, or escalation failures.
7 IDs. Facial recognition, surveillance, government-service access, police or border contexts, and AI-influenced administrative or corporate action.
7 IDs. Buses, vans, robotaxis, delivery robots, autonomous vehicles, clinical navigation systems, and automated alerts meeting physical consequences.
7 IDs. Agents or workflow tools acting inside repositories, file systems, cloud infrastructure, project caches, production databases, and task allocation.
5 IDs. AI-assisted spam, malicious skills, credential theft, unauthorized access, model distillation allegations, jailbreak-enabled theft, and adversarial AI operations.
3 IDs. Machine-learning risk systems, automated profiling, targeted marketing, and alleged exploitation of vulnerable gamblers.
The article is not only a count of cases; it is an argument about how AI harm becomes visible in public records.
The roundup warns that monitoring public reports remains only a fraction of unreported or underreported reality.
Many scam incidents depend on familiar local figures, institutions, languages, and media contexts.
The author describes concentrated searches in Southeastern European and Balkan sources to recover missed incidents.
The incidents route synthetic facsimiles of credibility toward illicit gain.
Fake medical endorsements can distort a person’s understanding of health choices as well as financial trust.
Operational agents can delete infrastructure, caches, drives, production databases, or user files when delegated work goes wrong.
Policing, immigration, surveillance, public service, and institutional decision incidents can alter a person’s relationship to public power.
Navigation, robotics, vehicle, and clinical systems translate software assumptions into physical consequences.
All 109 IDs are represented as RDF entities and linked here through URIBurner resolver links.
Questions and answers are named RDF resources.
The article reports 109 new incident IDs, ranging from 1362 through 1470.
No. The IDs were added during that window, but many document events from earlier dates.
Synthetic-media scams and consumer fraud was the largest category, with 22 IDs.
Because many missed incidents are localized in non-English media and local fact-checking sources.
They often misuse familiar people, medical authority, institutional brands, or local trust signals.
They are treated as misuse or adversarial activity rather than ordinary workflow malfunction.
Incident IDs are a structured snapshot of reported events, not a complete account of all AI harm.
Terms and definitions link into the RDF graph.
AI-generated or AI-manipulated media such as images, audio, video, or personas.
Fraud that uses synthetic likeness, voice, or institutional credibility to deceive people.
A seven-step workflow for reading an AI incident roundup as structured risk evidence.
Separate the date incident IDs were added from the dates on which reported events occurred.
Record the ID range, total count, and canonical AIID citation URL for each incident.
Map each incident to harm categories while preserving overlap notes where categories intersect.
Identify whether the incident relies on familiar people, institutions, languages, media, or communities.
Separate ordinary workflow or safety failures from adversarial, unauthorized, or malicious AI activity.
Mark cases involving state authority, healthcare, transportation, robotics, or other high-consequence settings.
Link every claim back to its source record and retain reported or alleged status instead of overclaiming certainty.