AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation
A systematic survey catalogues how LLM hallucinations corrupt vulnerability assessments — from phantom CVEs to fabricated severity scores — and proposes mitigation guardrails enterprises can layer into their toolchains.
Summary written by editorial AI · Source link below
arXiv:2608.25667v2 Announce Type: replace 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
Editorial Analysis
As AI-assisted vulnerability management becomes mainstream, hallucinated findings can silently distort risk registers; this survey gives security leaders a concrete taxonomy to calibrate trust in LLM outputs.
Introduce mandatory human-in-the-loop verification for any AI-generated vulnerability assessment before it influences patching priorities.
AI tools that produce fabricated vulnerability findings can misallocate security budgets — structured guardrails are now available to mitigate this risk.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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