What kind of vulnerability in the implementation of a vision-based machine learning system (object recognition, for example) would enable an attacker to achieve remote code execution?
The only instance I can think of is a vulnerability (e.g., buffer overflow) exploited in an ML-based text recognition system via malicious text input.
As far as I can tell, the only work that has been done to exploit AI/ML algorithms involves "adversarial" inputs that fool classifiers into an incorrect classification. These seem primarily useful for bypassing ML-based authentication and confusing systems like self-driving cars, but not for attacking the computers doing the classification.
Is there research being done on this topic, or is it unlikely until more standardized ML-based systems are deployed in the wild?