  Recently, deep learning has demonstrated promising results in enhancing the
accuracy of vulnerability detection and identifying vulnerabilities in
software. However, these techniques are still vulnerable to attacks.
Adversarial examples can exploit vulnerabilities within deep neural networks,
posing a significant threat to system security. This study showcases the
susceptibility of deep learning models to adversarial attacks, which can
achieve 100% attack success rate (refer to Table 5). The proposed method,
EaTVul, encompasses six stages: identification of important samples using
support vector machines, identification of important features using the
attention mechanism, generation of adversarial data based on these features
using ChatGPT, preparation of an adversarial attack pool, selection of seed
data using a fuzzy genetic algorithm, and the execution of an evasion attack.
Extensive experiments demonstrate the effectiveness of EaTVul, achieving an
attack success rate of more than 83% when the snippet size is greater than 2.
Furthermore, in most cases with a snippet size of 4, EaTVul achieves a 100%
attack success rate. The findings of this research emphasize the necessity of
robust defenses against adversarial attacks in software vulnerability
detection.
