Honeypot security technique can also stop attacks in natural language processing Borrowing a technique commonly used in cybersecurity to defend against these universal trigger-based attacks, researchers at the Penn State College of Information Sciences and Technology have developed a machine learning framework that can proactively defend against the same types of attacks in natural language processing applications 99% of the time. Image: Adobe Stock Honeypot security technique can also stop attacks in natural language processing Jessica Hallman July 28, 2021 UNIVERSITY PARK, Pa. — As online fake news detectors and spam filters become more sophisticated, so do attackers’ methods to trick them — including attacks through the “universal trigger.” In this learning-based method, an attacker uses a phrase or set of words to fool an indefinite number of inputs. A successful attack could mean more fake news appearing in your social media feed or spam reaching your email inbox.