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Automated Vulnerability Injection in Smart Contracts Using Large Language Models
AI InsightThis research shifts the role of LLMs from finding vulnerabilities to mass-producing annotated vulnerability samples, essentially building a more efficient evaluation infrastructure for smart contract security tools. Its value lies not in the effectiveness of injecting a single vulnerability, but in transforming manual annotation from a bottleneck into a scalable pipeline, potentially accelerating the iteration and validation of security tools.Key TakeawaySmart contract vulnerability datasets are shifting from manual construction to LLM-based automated injection.Why It MattersEvaluating vulnerability detection tools relies on datasets with known ground truth, which are costly and limited to build manually. If LLM-based automated injection can scale up valid samples, it will lower evaluation barriers and improve the reliability and coverage of security tools.Who's Affected- Smart Contract Security Tool DevelopersCan obtain richer, annotated test sets to validate and optimize detection models.
- Blockchain Auditing FirmsMay train more accurate auditing tools using such datasets, improving efficiency.
- Smart Contract DevelopersAutomatically generated vulnerable contracts could be exploited or used for testing, so tool reliability needs attention.
What's NextFuture attention should focus on how the generated vulnerable contracts perform on real-world detection benchmarks and whether they are incorporated into training or evaluation sets of mainstream vulnerability detection tools.Importance 68/100