  Vulnerabilities in software security can remain undiscovered even after being
exploited. Linking attacks to vulnerabilities helps experts identify and
respond promptly to the incident. This paper introduces VULDAT, a
classification tool using a sentence transformer MPNET to identify system
vulnerabilities from attack descriptions. Our model was applied to 100 attack
techniques from the ATT&CK repository and 685 issues from the CVE repository.
Then, we compare the performance of VULDAT against the other eight
state-of-the-art classifiers based on sentence transformers. Our findings
indicate that our model achieves the best performance with F1 score of 0.85,
Precision of 0.86, and Recall of 0.83. Furthermore, we found 56% of CVE reports
vulnerabilities associated with an attack were identified by VULDAT, and 61% of
identified vulnerabilities were in the CVE repository.
