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Jianshuo Dong

Jianshuo Dong appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Cryptography and SecurityArtificial IntelligenceComputation and Language

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Published work

2 published item(s)

preprint2026arXiv

Can Large Language Models Automate the Refinement of Cellular Network Specifications?

Cellular networks, e.g., 4G/5G, rely on complex technical specifications to ensure correct functionality; however, these specifications often contain flaws or ambiguities. In this paper, we investigate the application of Large Language Models for automated cellular network specification refinement. We identify Change Requests, which record specification revisions, as a key source of domain-specific data and formulate specification refinement as three complementary sub-tasks. We introduce CR-Eval, a benchmark of 200 security-related test cases, and evaluate 17 open-source and 14 proprietary models. The best-performing model, GPT-o3-mini, identifies weaknesses in over 127 test cases within five trials. We further study LLM specialization, showing that fine-tuning an 8B model can outperform advanced LLMs such as DeepSeek-R1 and Qwen3-235B. Evaluations on 30 real-world cellular attacks demonstrate the practical impact and remaining challenges. The codebase and benchmark are available at https://github.com/jianshuod/CR-Eval.

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preprint2026arXiv

LeakDojo: Decoding the Leakage Threats of RAG Systems

Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to leverage external knowledge, but also exposes valuable RAG databases to leakage attacks. As RAG systems grow more complex and LLMs exhibit stronger instruction-following capabilities, existing studies fall short of systematically assessing RAG leakage risks. We present LeakDojo, a configurable framework for controlled evaluation of RAG leakage. Using LeakDojo, we benchmark six existing attacks across fourteen LLMs, four datasets, and diverse RAG systems. Our study reveals that (1) query generation and adversarial instructions contribute independently to leakage, with overall leakage well approximated by their product; (2) stronger instruction-following capability correlates with higher leakage risk; and (3) improvements in RAG faithfulness can introduce increased leakage risk. These findings provide actionable insights for understanding and mitigating RAG leakage in practice. Our codebase is available at https://github.com/yeasen-z/LeakDojo.

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