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intelligence_src_f28777e1849bec7cf0
observed · 2026-10-12 16:59:03 UTC
This article is brought to you by VicOne.Robot safety has traditionally asked: Can a machine remain safe when something goes wrong? Physical AI raises a harder question: Can a machine remain safe when an attacker changes what it sees, decides, or does even when nothing appears to have failed?As AI and robotics continue to advance at an unprecedented pace, modern robots perceive through multimodal sensors, interpret context using AI models, and translate those interpretations into physical action. As they move into dynamic environments, their safety increasingly depends on the integrity of the data guiding their decisions.That dependence creates risks that conventional safety assessments may not fully capture. Recent research has demonstrated that manipulating what a robot sees, hears, or interprets can influence its behavior without requiring direct control.Such manipulation can occur anywhere across its complex sensing and decision-making system — a layered attack surface encompassing training pipelines, system infrastructure, and runtime perception.Layer One: Corrupting intelligence at its sourceIn 2017, BadNets demonstrated that a model could behave normally under most conditi
Fingerprint: fa340eee0beb9906ed8da5277eac55337fa390b3ad1a4c077025bf37dcf9c5ef
intelligence_src_f28777e1849bec7cf0
observed · 2026-10-12 16:59:03 UTC
available
Fingerprint: 6ab66bf670ba3f01aa469a19a61354be9b2f1c174649ee36eccd41ac913c914b