A Grey-box Text Attack Framework using Explainable AI

Explainability tools can be weaponized: The paper reveals that the same explainable AI techniques designed to make models trustworthy and transparent can be exploited to identify and attack vulnerabilities in AI systems, creating a potential security trade-off between transparency and robustness.

Adversarial attacks can be subtle and transferable: Unlike obvious word substitutions that humans easily detect, this framework generates more natural-sounding adversarial text that transfers across different AI models (e.g., from one BERT variant to another), making attacks harder to identify and defend against.

You don't need full model access to attack AI: The "grey-box" approach requires only surrogate models rather than direct access to the target system, meaning attackers can potentially compromise AI systems without insider knowledge—a significant practical security concern for deployed models.

Transformer architecture's strength becomes a weakness: The attention mechanism that allows transformer models like BERT to understand language context also enables adversarial attacks to transfer between different models, suggesting that the very features making these systems powerful may also make them vulnerable.

Current AI security may be insufficient: The paper demonstrates that existing defenses against adversarial text attacks may be inadequate, particularly when attackers use explainability methods strategically, highlighting the need for new security measures alongside transparency initiatives.