Adversarial Attacks Challenge the Integrity of Speech Language Models | HackerNoon

The article examines the vulnerabilities of Spoken QA systems, particularly against adversarial attacks. It outlines the attack methodologies for both white-box and black-box scenarios, emphasizing how attackers can exploit these models using techniques like projected gradient descent. The work also explores countermeasures to safeguard against these attacks, ultimately demonstrating the need for more resilient defenses in the face of increasing threats to speech language models (SLMs). The discussion integrates established literature and recent studies to strengthen the understanding of both attack mechanisms and preventive strategies.

In our work, we illustrate how to adapt established adversarial attack methodologies to address vulnerabilities in Spoken QA systems, focusing on jailbreaking SLMs.

White-box attacks allow attackers full access to the model, enabling them to leverage methods like projected gradient descent for effective adversarial optimization.

Read at Hackernoon