Part 8/10:
In the meantime, the implications are clear: AI developers and deployers must account for the risks of small-scale data poisoning. This could include more stringent data vetting, monitoring for unusual triggers, and developing robust model architectures that are resistant to such manipulations.
The Broader Societal Impact
This research doesn’t just mean that models can be tricked—it's also about the potential for subtle influence over behavior. Malicious actors could generate sophisticated misinformation campaigns, craft biased content, or insidiously influence public perception by strategically poisoning data sources.