aiqbench analysis
This research leverages Large Language Models to automate the injection of vulnerabilities into Solidity smart contracts, addressing the scarcity of ground-truth datasets needed to evaluate security tools. The study validates a pipeline that successfully generated hundreds of candidate variants, revealing both the feasibility of AI-driven fuzzing and significant challenges regarding non-determinism and semantic preservation. By applying these AI-generated samples to static analyzers, the work exposes critical gaps in current coverage profiles for smart contract security assessment.
Automated Vulnerability Injection in Smart Contracts Using Large Language Models
arXiv:2609.02624v1 Announce Type: cross Abstract: Assessing vulnerability detection tools for smart contracts requires datasets with known ground truth, yet such datasets are scarce and difficult to build by hand. We propose an approach that uses Large Language Models (LLMs) to automatically inject vulnerabilities into Solidity smart contracts, and demonstrate it in a case study targeting 49 vulnerability types from OpenSCV. Injected contracts are validated through a multi-step pipeline checking compilation, execution, business logic, and the presence of the intended vulnerability. Applied to real-world contracts from SmartBugs, LLMs generate nearly 1,000 candidate variants; after deduplication and validation, 32 confirmed vulnerable contracts spanning 25 vulnerability types survive (a 16.58% survival rate). Surviving contracts concentrate in structurally simpler targets and vulnerability types with localized syntactic patterns. We report practical challenges including LLMs' non-determinism and the difficulty of preserving contract semantics. We then use the validated contracts to assess three static analyzers, revealing complementary and incomplete coverage profiles. Results show that LLM-based vulnerability injection is feasible, while exposing key limitations in scalability and diversity.
Abstract:Assessing vulnerability detection tools for smart contracts requires datasets with known ground truth, yet such datasets are scarce and difficult to build by hand. We propose an approach that uses Large Language Models (LLMs) to automatically inject vulnerabilities into Solidity smart contracts, and demonstrate it in a case study targeting 49 vulnerability types from OpenSCV. Injected contracts are validated through a multi-step pipeline checking compilation, execution, business logic, and the presence of the intended vulnerability. Applied to real-world contracts from SmartBugs, LLMs generate nearly 1,000 candidate variants; after deduplication and validation, 32 confirmed vulnerable contracts spanning 25 vulnerability types survive (a 16.58% survival rate). Surviving contracts concentrate in structurally simpler targets and vulnerability types with localized syntactic patterns. We report practical challenges including LLMs' non-determinism and the difficulty of preserving contract semantics. We then use the validated contracts to assess three static analyzers, revealing complementary and incomplete coverage profiles. Results show that LLM-based vulnerability injection is feasible, while exposing key limitations in scalability and diversity.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
arXiv:2609.02624 [cs.SE]
(or arXiv:2609.02624v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2609.02624 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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