Static analysis tools have evolved over time to assist in detecting bugs. However, the excessive false warnings can impede developers’ productivity and confidence in the tools. Previous research efforts have explored learning-based approaches to identify bug warnings. Nevertheless, their coarse granularity, focusing on either long-term warnings or function-level alerts, are insensitive to individual bugs. Also, they rely on manually crafted features or solely on source code semantics, which is inadequate for effective learning. In this paper, we propose DeepFWI, a learning-based approach that identifies bug-sensitive warnings at a fine-grained granularity. Specifically, we design a novel LSTM-based model that captures multi-modal semantics of source code and warnings from automated static analysis tools (ASATs) and highlights their correlations with cross-attention. To tackle the data scarcity of training and evaluation, we collected a large-scale dataset of 280,273 warnings. We conducted extensive experiments on the dataset to evaluate DeepFWI. The experimental results demonstrate the effectiveness of our approach, with an F1-score 67.06% for confirming true warnings in a finer-grained manner, significantly outperforming all baselines. Additionally, to validate the practicality of DeepFWI from the perspective of developers, we applied DeepFWI to four popular open-source projects. Our approach filtered out the vast majority of warnings, while still successfully surfacing 25 true bug-related warnings that were confirmed through manual analysis.

Access control (AC) vulnerabilities are among the most critical security threats to smart contracts. Despite extensive research, they remain widespread and damaging in the Ethereum ecosystem. To understand and advance the current state-of-the-art (SOTA) in AC vulnerability detection, we first curate a diverse dataset of 180 real-world AC vulnerabilities from CVE entries, DeFiHackLabs incidents, and Code4rena audit reports.Using this dataset, we conduct a systematic benchmark study along three dimensions. First, we develop a cause-based taxonomy and analyze the prevalence and evolution of AC vulnerabilities. Second, we evaluate six SOTA tools, including two from industry and four from academia, revealing low recall (3% to 8%) and significant blind spots. To understand these failures, we examine 1.2 million deployed contracts and uncover practical gaps in AC protection mechanisms overlooked by existing tools. Finally, we assess the potential of large language models (LLMs) for AC vulnerability detection and show that LLMs detect 53–75% of vulnerabilities, outperforming traditional tools but facing challenges such as hallucinations and scalability. Our findings highlight the need for hybrid approaches that combine static analysis with LLM-based semantic reasoning to address the complexity of modern AC vulnerabilities.

OpenZeppelin is a building block for many smart contracts on Ethereum-compatible blockchains. It provides mod-ular and reusable libraries for various Ethereum standards (e.g., ERC20 and ERC721) and common functionalities such as upgradeable contracts. Little research has been done on Open-Zeppelin security except for a recent study, which focused only on the misuse of OpenZeppelin code, assuming OpenZeppelin itself is secure but contract developers may not follow OpenZeppelin’s function checks appropriately. We argue that, despite appearing robust, OpenZeppelin itself could have many vulnerabilities, and these library-level vulnerabilities could inadvertently affect third-party smart contracts, even without misuse from developers.We present ZepCompare, the first end-to-end system for demystifying OpenZeppelin’s own vulnerabilities and analyzing their propagation in third-party smart contracts. ZepCompare incorporates a manual analysis stage where we review OpenZeppelin’s 64 historical releases, identifying 109 vulnerable-fixed code pairs, exposing flaws in cryptographic utilities, access control, etc. Leveraging these pairs, ZepCompare introduces facts of changes, a novel structure capturing vulnerable and fixed code contexts for flexible matching. Evaluated across 88,605 contracts from three Ethereum-compatible chains, ZepCompare detects 4,708 instances of OpenZeppelin-derived vulnerabilities. Manual sampling and a ground-truth experiment confirm that ZepCompare achieves 86.7% precision and 77.1% recall. Our findings reveal significant security risks in both historical and the latest versions of OpenZeppelin libraries, underscoring the urgent need for systematic auditing of foundational contracts components.

Smart contracts are prone to various vulnerabilities, leading to substantial financial losses over time. Current analysis tools mainly target vulnerabilities with fixed control- or data-flow patterns, such as re-entrancy and integer overflow. However, a recent study on Web3 security bugs revealed that about 80% of these bugs cannot be audited by existing tools due to the lack of domain-specific property description and checking. Given recent advances in Large Language Models (LLMs), it is worth exploring how Generative Pre-training Transformer (GPT) could aid in detecting logic vulnerabilities.

Open-source software (OSS) vulnerabilities are increasingly prevalent, emphasizing the importance of security patches. However, in widely used security platforms like NVD, a substantial number of CVE records still lack trace links to patches. Although rank-based approaches have been proposed for security patch tracing, they heavily rely on handcrafted features in a single-step framework, which limits their effectiveness. In this paper, we propose PatchFinder, a two-phase framework with end-to-end correlation learning for better-tracing security patches. In the initial retrieval phase, we employ a hybrid patch retriever to account for both lexical and semantic matching based on the code changes and the description of a CVE, to narrow down the search space by extracting those commits as candidates that are similar to the CVE descriptions. Afterwards, in the re-ranking phase, we design an end-to-end architecture under the supervised fine-tuning paradigm for learning the semantic correlations between CVE descriptions and commits. In this way, we can automatically rank the candidates based on their correlation scores while maintaining low computation overhead. We evaluated our system against 4,789 CVEs from 532 OSS projects. The results are highly promising: PatchFinder achieves a Recall@10 of 80.63% and a Mean Reciprocal Rank (MRR) of 0.7951. Moreover, the Manual Effort@10 required is curtailed to 2.77, marking a 1.94 times improvement over current leading methods. When applying PatchFinder in practice, we initially identified 533 patch commits and submitted them to the official, 482 of which have been confirmed by CVE Numbering Authorities.

Quality assurance (QA) tools are receiving more and more attention and are widely used by developers. Given the wide range of solutions for QA technology, it is still a question of evaluating QA tools. Most existing research is limited in the following ways: (i) They compare tools without considering scanning rules analysis. (ii) They disagree on the effectiveness of tools due to the study methodology and benchmark dataset. (iii) They do not separately analyze the role of the warnings. (iv) There is no large-scale study on the analysis of time performance. To address these problems, in the paper, we systematically select 6 free or open-source tools for a comprehensive study from a list of 148 existing Java QA tools. To carry out a comprehensive study and evaluate tools in multi-level dimensions, we first mapped the scanning rules to the CWE and analyze the coverage and granularity of the scanning rules. Then we conducted an experiment on 5 benchmarks, including 1,425 bugs, to investigate the effectiveness of these tools. Furthermore, we took substantial effort to investigate the effectiveness of warnings by comparing the real labeled bugs with the warnings and investigating their role in bug detection. Finally, we assessed these tools’ time performance on 1,049 projects. The useful findings based on our comprehensive study can help developers improve their tools and provide users with suggestions for selecting QA tools.

Static application security testing (SAST) takes a significant role in the software development life cycle (SDLC). However, it is challenging to comprehensively evaluate the effectiveness of SAST tools to determine which is the better one for detecting vulnerabilities. In this paper, based on well-defined criteria, we first selected seven free or open-source SAST tools from 161 existing tools for further evaluation. Owing to the synthetic and newly-constructed real-world benchmarks, we evaluated and compared these SAST tools from different and comprehensive perspectives such as effectiveness, consistency, and performance. While SAST tools perform well on synthetic benchmarks, our results indicate that only 12.7% of real-world vulnerabilities can be detected by the selected tools. Even combining the detection capability of all tools, most vulnerabilities (70.9%) remain undetected, especially those beyond resource control and insufficiently neutralized input/output vulnerabilities. The fact is that although they have already built the corresponding detecting rules and integrated them into their capabilities, the detection result still did not meet the expectations. All useful findings unveiled in our comprehensive study indeed help to provide guidance on tool development, improvement, evaluation, and selection for developers, researchers, and potential users.