LLM4Trust: Exploring the Capabilities of Large Language Models for Trust Evaluation

The paper introduces LLM4Trust, a benchmark framework that evaluates large language models (LLMs) for trust assessment in cybersecurity. It constructs trust graphs to represent five fundamental trust properties, designs property‑understanding tasks, and tests eight LLMs across nine prompting strategies. The study identifies effective LLM‑prompt combinations, applies them to real datasets, proposes methods to handle context limits, and demonstrates that LLMs can understand trust properties while highlighting vulnerabilities and high inference costs, for which defense mechanisms and batch inference are suggested.

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• llms
• benchmarks