Information Flow vs. Code: The Blind Spot of AI Security

This Habr article explores a critical vulnerability in modern AI systems related to data pollution. The author argues that current security measures, such as code-level guardrails, fail to address systemic instability because models are trained on data streams where synthetic content is indistinguishable from human-generated input. Issues like prompt injection and the inability of agents to verify information sources create a risk of 'information collapse.' As models increasingly train on their own output, the quality of their decision-making inevitably degrades, potentially leading to global consequences comparable to financial crises. The author emphasizes that AI security depends not only on code but also on the integrity of global communication. The only way to prevent model degradation is to prioritize human data over synthetic data, yet current market trends are moving in the opposite direction, posing significant risks to the future of technological development.