paperAugust 2026Unreviewed

AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation

Junchen Ding, Jialiang Dong, Yi Zhu, Yi Liu, Gelei Deng, William Susilo, Simeng Ma, Yuekang Li

Abstract

The integration of Large Language Models (LLMs) into cybersecurity has transformed vulnerability assessment, but it has also produced a trustworthiness crisis driven by the unchecked proliferation of"AI slop."These artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports, impose a cognitive burden on human triage pipelines that mirrors a denial-of-service attack. This paper surveys the empirical evidence, identifies a unifying mechanism, a

Cite

@misc{ding2026ai,
  title = {{AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation}},
  author = {Junchen Ding and Jialiang Dong and Yi Zhu and Yi Liu and Gelei Deng and William Susilo and Simeng Ma and Yuekang Li},
  year = {2026},
  month = aug,
  eprint = {2608.25667},
  archivePrefix = {arXiv},
  url = {https://www.semanticscholar.org/paper/a4d1c9973548a5f990db11f16b8e5d1070768d62}
}