LATIDIA · Ciberseguridad

Errores y desviaciones en la IA durante la evaluación de vulnerabilidades: un estudio sobre fallos de razonamiento y medidas de mitigación fiables.

arXiv:2608.25667v2 Tipo de anuncio: reemplazar Resumen: La integración de modelos de lenguaje grandes (LLM) en la ciberseguridad ha transformado la evaluación de vulnerabilidades, pero también ha producido una crisis de confiabilidad impulsada por

La noticia

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 empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage. We formalize a taxonomy of AI slop grounded in a structured literature review and dissect its root cause: the gap between the causal deductive reasoning of security experts and the autoregressive probabilistic generation