DINA: A Dual Defense Framework Against Internal Noise and External Attacks in Natural Language Processing

AI systems face threats from both inside and outside: The research identifies two distinct vulnerabilities in language models used for customer service and content moderation—external attacks (deliberate manipulation of inputs) and internal corruption (mislabeled training data). This dual threat requires simultaneous defense strategies rather than addressing them separately.

Practical solutions exist for real-world deployment: DINA, the framework presented, combines techniques from computer vision with adversarial training methods and demonstrates "significant improvements in model robustness and accuracy" on real data from an online gaming service, suggesting these defenses can be implemented in actual production systems.

Label corruption is a serious but addressable vulnerability: The paper highlights that internal label sabotage—where training data is intentionally or unintentionally mislabeled—poses a genuine risk to AI systems. The research shows this threat can be mitigated through adapted noisy-label learning methods, making it a concrete concern organizations should monitor.

Responsible AI requires anticipating adversarial scenarios: The framework's focus on "realistic adversarial scenarios" emphasizes that deploying AI responsibly means proactively testing systems against both accidental data quality issues and intentional attacks, not just assuming models will perform as designed in the real world.