VerilogLAVD: LLM-Aided Pattern Generation for Verilog CWE Detection (2026.acl-long)
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| Challenge: | Existing static analysis tools focus on functional correctness and depend heavily on manual rules. |
| Approach: | They propose a framework that generates executable Traversal Detection Patterns (TDPs) to help detect hardware vulnerabilities. |
| Outcome: | The proposed framework improves the F1 score by 133% compared to LLM-based methods. |
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| Challenge: | Large language models (LLMs) are capable of detecting software vulnerabilities, but lack of reasoning data hinders their ability to capture underlying vulnerability patterns. |
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| Challenge: | Existing training-free detectors rely on global statistics of the Token Perplexity Sequence (TPS) and struggle with code. |
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LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have a high vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs. |
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RealVul: Can We Detect Vulnerabilities in Web Applications with LLM? (2024.emnlp-main)
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Know Thy Enemy: Securing LLMs Against Prompt Injection via Diverse Data Synthesis and Instruction-Level Chain-of-Thought Learning (2026.findings-acl)
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Zhiyuan Chang, Mingyang Li, Yuekai Huang, Ziyou Jiang, Xiaojun Jia, Qian Xiong, Junjie Wang, Zhaoyang Li, Qing Wang
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Pattern Enhanced Multi-Turn Jailbreaking: Exploiting Structural Vulnerabilities in Large Language Models (2026.findings-acl)
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| Challenge: | Existing multi-turn methods for large language models exploit conversational context to bypass safety constraints gradually. |
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Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown promise in software vulnerability detection, especially on function-level benchmarks like Devign and BigVul. |
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Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks (2024.lrec-main)
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| Challenge: | Existing methods to jailbreak large language models have been poorly studied . a recent study showed that non-expert users can jailbreak LLMs by manipulating their prompts . |
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A Survey on Detection of LLMs-Generated Content (2024.findings-emnlp)
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Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng
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DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection (2026.acl-long)
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Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang, Weihua Luo, Jinsong Su, Derek F. Wong
| Challenge: | Existing detectors are limited in their ability to detect large language models generated content in multilingual environments. |
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