F-Secure maps 26 ways AI can harm consumers
Original title: F-Secure Identifies 26 Ways AI Can Harm Consumers, and Where to Stop Them
The taxonomy covers five relationship types and 19 intermediary layers, with 24 defenses at different points.
F-Secure published an AI Harms Taxonomy that maps 26 distinct consumer harms and traces how each reaches a person, from misleading chatbot answers to compromised autonomous agents. Developed by Dr. Megan Squire, it links harms to systems and companies that could intervene rather than only listing possible failures. Cited examples include AI-generated voice and video impersonation, agents making harmful financial decisions, and exposure of personal information. The research also covers assistants manipulated into causing damage and chatbots that mislead with confidence.
Unconfirmed: The available text cuts off before listing every harm and defense.
- The taxonomy was announced in Los Angeles on November 10, 2026.
- It maps 26 harms, five relationship types, 19 layers and 24 defenses.
- Dr. Megan Squire, F-Secure's principal threat intelligence researcher, developed it.
- Harm often starts in instruction handling, memory or connected services.
- Organizations
- F-Secure
From the original article
7 min read at darkreading.com
New AI Harms Taxonomy traces how harm moves through AI systems, from misleading chatbot answers to compromised autonomous agents, and identifies where defenses can intervene.
darkreading.com
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