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

The paper examines how Large Language Models (LLMs) have introduced a trustworthiness crisis in cybersecurity vulnerability assessment, producing artifacts known as "AI slop" such as hallucinated vulnerabilities and incorrect patches. It presents a taxonomy of these failures, attributes them to a mismatch between deductive reasoning and probabilistic generation, and introduces the Deductive Coverage Score as a measurable proxy. The authors review mitigation strategies, critique passive approaches, and propose active neuro‑symbolic verification, concluding with new evaluation tools—CVE‑Bench and Slop‑Score—to shift focus from linguistic fluency to mathematical verifiability.

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• llms
• agents
• safety