Evaluating Large Language Model Application Impacts on Evasive Spectre Attack Detection

This paper investigates the impact of different Large Language Models (DeepSeek, Kimi and Doubao) on the attack detection success rate of evasive Spectre attacks while accessing text, image, and code tasks. By running different Large Language Models (LLMs) tasks concurrently with evasive Spectre attacks, a unique dataset with LLMs noise was constructed. Subsequently, clustering algorithms were employed to reduce the dimension of the data and filter out representative samples for the test set. Finally, based on a random forest detection model, the study systematically evaluated the impact of different task types on the attack detection success rate. The experimental results indicate that the attack detection success rate follows the pattern of “code > text > image” in both the evasive Spectre memory attack and the evasive Spectre nop attack. To further assess the influence of different architectures on evasive Spectre attacks, additional experiments were conducted on an NVIDIA RTX 3060 GPU. The results reveal that, on the RTX 3060, the attack detection success rate for code tasks decreased, while those for text and image tasks increased compared to the 2080 Ti. This finding suggests that architectural differences impact the manifestation of Hardware Performance Counters (HPCs), influencing the attack detection success rate.