Stealthy Backdoor Attacks in NLP: Low-Cost Poisoning and Evasion

This ArXiv paper highlights a critical vulnerability in NLP models, demonstrating how attackers can subtly inject backdoors with minimal effort. The research underscores the need for robust defense mechanisms against these stealthy attacks.

• •Steganographic backdoors allow for ultra-low poisoning rates, making detection difficult.
• •The attacks are designed for defense evasion, meaning they can bypass existing security measures.
• •The research emphasizes the need for proactive security measures in NLP models.