Blockchain has been widely adopted across diverse sectors. Yet, enterprise-grade systems remain vulnerable to critical flaws that undermine stability and security. Although academic fuzzing tools such as LOKI and Tyr have shown effectiveness in detecting such issues, their integration into industrial practice remains challenging.

As critical infrastructure for Web 3.0, DAG-based blockchains promise high throughput for DeFi, IoT, and DApps. However, realizing this potential is challenging, as system performance is dictated by a multitude of interdependent parameters across network, node, and consensus layers. Manual configuration fails to adapt to dynamic workloads, leading to suboptimal performance. We introduce Alzo, a novel auto-tuner that employs hierarchical reinforcement learning (HRL) to navigate this complex configuration space. By decomposing the DAG blockchain's workflow into distinct stages, Alzo's HRL policy learns from stage-level performance metrics to control critical parameters governing consensus, execution, and graph topology in real-time. Furthermore, we employ a shadow-control loop to ensure the safety of all parameter adjustments. Our experiments show that Alzo significantly outperforms other configurations, achieving higher throughput and lower latency under variable workloads with minimal overhead.

The rapid growth of blockchain technology has driven the widespread adoption of smart contracts; however, their inherent vulnerabilities have led to significant financial losses. Traditional auditing methods, while essential, struggle to keep pace with the increasing complexity and scale of smart contracts. Large language models (LLMs) offer promising capabilities for automating vulnerability detection, but their adoption is often limited by high computational costs. Although prior work has explored leveraging large models through agents or workflows, relatively little attention has been given to improving the performance of smaller, fine-tuned models—a critical factor for achieving both efficiency and data privacy. In this paper, we introduce HKT-SmartAudit, a framework for developing lightweight models optimized for smart contract auditing. It features a multi-stage knowledge distillation pipeline that integrates classical distillation, external domain knowledge, and reward-guided learning to transfer high-quality insights from large teacher models. A single-task learning strategy is employed to train compact student models that maintain high accuracy and robustness while significantly reducing computational overhead. Experimental results show that our distilled models outperform both commercial tools and larger models in detecting complex vulnerabilities and logical flaws, offering a practical, secure, and scalable solution for smart contract auditing. The source code is available in the GitHub repository1.

The transaction execution layer is a key determinant of throughput in permissioned blockchains. While recent Shared Memory Pools (SMP)-based approaches improve throughput by enabling all consensus nodes to participate in transaction packaging, they face two fundamental limitations. First, the performance bottleneck shifts from the consensus layer to the transaction execution layer as transaction number confirmed in a round increases. Second, these approaches are vulnerable to “transaction duplication” attacks where malicious clients can simultaneously send the same transaction to multiple consensus nodes, thereby decreasing the number of valid transactions in block proposals. To address these limitations, this paper introducesNexus, a novel blockchain transaction processing framework with high scalability.Nexusleverages the idle computational resources of full nodes to enable transaction execution in parallel with the consensus. Moreover,Nexusallows each node to handle only a fraction of the total transactions and share execution results with others. This approach reduces overall transaction execution time, increases throughput, and decreases latency. Lastly,Nexusintroduces a transaction partitioning mechanism that effectively addresses the “transaction duplication” attack and achieves load balancing between clients and consensus nodes. Our implementation ofNexusdemonstrates significant improvements: throughput increases by 4x to 15x, and latency is reduced by 50% to 70%.

Blockchain’s inherent immutability, while transformative, creates critical security risks in smart contracts, where undetected vulnerabilities can result in irreversible financial losses. Current auditing tools and approaches often address specific vulnerability types, yet there is a need for a comprehensive solution that can detect a wide range of vulnerabilities with high accuracy. We propose LLM-SmartAudit, a novel framework that leverages Large Language Models (LLMs) to automate smart contract vulnerability detection and analysis. Using a multi-agent conversational architecture with a buffer-of-thought mechanism, LLM-SmartAudit maintains a dynamic record of insights generated throughout the audit process. This enables a collaborative system of specialized agents to iteratively refine their assessments, enhancing the accuracy and depth of vulnerability detection. To evaluate its effectiveness, LLM-SmartAudit was tested on three datasets: a benchmark for common vulnerabilities, a real-world project corpus, and a CVE dataset. It outperformed existing tools with 98% accuracy on common vulnerabilities and demonstrates higher accuracy in real-world scenarios. Additionally, it successfully identifies 12 out of 13 CVEs, surpassing other LLM-based methods. These results demonstrate the effectiveness of multi-agent collaboration in automated smart contract auditing, offering a scalable, adaptive, and highly efficient solution for blockchain security analysis.

Rust is designed as a systems programming language that aims to provide safety guarantees and performance efficiency. In practice, programmers usually use atomic correlations to share data across threads. For example, by using atomic operations to correlate with non-atomic addresses, they can design lock-free data structures for efficient concurrency. Although atomic operations are used in safe code, memory ordering misuses can still lead to atomic concurrency bugs and performance loss.In this paper, we conduct the first empirical study of atomic operations and memory ordering usage in Rust, manual inspection of 2883 atomic usages in real-world applications, including 15 thread bugs and 150 performance issues. We also study their usage scenarios, performance comparisons and issue fixes to provide a better understanding on Rust’s memory ordering misuses and guide better code practices in the future.We design AtomVChecker, an automated static analyzer to detect memory ordering misuses. we evaluate our tool on four widely-used concurrent libraries, it can automatically analyze 228 atomic correlations with 80% accuracy. Based on the atomic correlation analysis, AtomVChecker finds a total of 51 performance loss issues in 9 Rust packages, with all of them recently confirmed by the project maintainer based on our reports.

Blockchain technology builds an immutable and append-only ledger in peer-to-peer networks, which attracts attention from various fields. However, traditional chain-based blockchain systems typically have the problem of low throughput, leading to unsatisfactory performance. Among the proposed solutions, introducing a structure of the Directed Acyclic Graph (DAG) into the blockchain reaches a high transaction throughput. Such an approach enables blocks to refer to more than one previous block, thus processing blocks in parallel with better performance. However, existing DAG-based blockchain schemes do not establish a deterministic rule for block reference priority. Adversaries can initiate a splitting attack to select block references to affect DAG topology, making the consensus unstable. In this paper, we propose a more stable consensus protocol named Phantasm, aiming to stabilize the ordering result in the consensus protocol. The referred blocks can be decided after computing a solution to the block puzzle and the difficulty of this solution affects the number of block references. We design two strategies to guide the honest nodes to select references so that they can resist the splitting attacks to stabilize the ordering. Theoretical analysis and simulation experiments show that Phantasm is more stable than the classic DAG-based blockchain consensus protocol Phantom regarding the ordering results.