ContractLens: Latent Security Knowledge for Malicious Smart Contract Detection
cs.CR
Submitted: 2026-10-04
Updated: 2026-10-04
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Mechanistic Interpretability for AI Safety -- A Review
- Which Neurons Detect Malicious Code? A Probing Study of LLM Security Knowledge
- LogicScan: An LLM-driven Framework for Detecting Business Logic Vulnerabilities in Smart Contracts
- Gemma: Open Models Based on Gemini Research and Technology
- Finding Neurons in a Haystack: Case Studies with Sparse Probing
- Qwen2.5-Coder Technical Report
- Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion
- LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
- CodeGemma: Open Code Models Based on Gemma
- FTSmartAudit: A Knowledge Distillation-Enhanced Framework for Automated Smart Contract Auditing Using Fine-Tuned LLMs
- LLM-SmartAudit: Advanced Smart Contract Vulnerability Detection
- Qwen3 Technical Report
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