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December 21, 2025Open Access

SecReEvalBench: A Multi-turned Security Resilience Evaluation Benchmark for Large Language Models

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Authors

HCHuijuan CuiWLWei Liu

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Overview

SecReEvalBench evaluates security domains in large language models, suggesting improvements in adversarial attack defenses.

Key Points

  • This research presents a benchmark aimed at evaluating the resilience of large language models against adversarial prompts.
  • Introduces SecReEvalBench with metrics for assessing resilience to attacks.
  • Evaluates models using a dataset with neutral and malicious prompts.
  • Analyzes five large language models: Llama 3.1, Gemma 2, Mistral v0.3, DeepSeek-R1, and Qwen 3.
  • Provides insights into the effectiveness of large language models against various adversarial strategies.
  • Identifies strengths and weaknesses in model defenses against evolving threats.

Cite This Study

Cui et al. (2025) studied this question.

synapsesocial.com/papers/69473b64db9c958d0dfca951https://doi.org/10.48550/arxiv.2505.07584
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  2. 2CS-Eval—A Concise Benchmark for Evaluating the Security Risks of Large Language Models2024 · 1 citations
  3. 3Evaluating the Cybersecurity Robustness of Commercial LLMs against Adversarial Prompts: A PromptBench Analysis2024 · 3 citations
  4. 4SECURE: Benchmarking Generative Large Language Models for Cybersecurity Advisory2024 · 2 citations
  5. 5S-Eval: Automatic and Adaptive Test Generation for Benchmarking Safety Evaluation of Large Language Models2024 · 1 citations