Daoyuan Wu

Large language models (LLMs) exhibit impressive capabilities but are susceptible to adversarial attacks that induce harmful outputs. Although various defenses have been proposed, their practicality is restricted by substantial runtime overhead or degraded model helpfulness. Moreover, LLM applications typically have diverse and evolving security requirements that cannot be fully anticipated during the design of static defenses. These limitations call for a flexible, low-overhead defense mechanism that can be easily customized to meet task-specific needs. In this paper, we explore function calling (FC)—a built-in mechanism in modern LLMs for invoking custom tools—as a lightweight and adaptable defense add-on. We show that by defining functions representing malicious actions, LLMs equipped with FC can intercept harmful prompts by triggering these function calls instead of generating unsafe content. Extensive experiments across mainstream LLMs demonstrate that FC substantially improves defense effectiveness with minimal impact on model helpfulness. To further assess FC's practical utility, we also introduce DSPEC, a new dataset reflecting real-world LLM applications with specific defense requirements. Our evaluations on DSPEC show that FC substantially outperforms existing defenses in this realistic setting. Besides, we also explore the practical applications of FC in various scenarios, including universal defense frameworks and multi-agent systems, further demonstrating its versatility and effectiveness in enhancing LLM security.

Decompiling stripped binaries to assist human reverse engineers remains a critical yet highly challenging task in software security. Prior work has developed various neural networks and even dedicated large language models (LLMs) to improve function and variable name recovery in decompiled code. Nonetheless, these approaches alone fall short of enabling a generic LLM-based decompilation pipeline that can reliably enhance the readability and semantic clarity of decompiled outputs. In this paper, we propose BinRAG, a retrieval-augmented generation (RAG) based decompilation framework designed to enhance the decompilation of stripped binaries. Building upon name prediction models, BinRAG features three novel designs: (1) it first utilizes name prediction models to transform raw decompiled outputs into enriched, source-like queries for RAG retrieval; (2) it further enhances these queries using a fine-tuned specialized LLM conditioned on the predicted variable names, enabling more accurate retrieval from a curated example database; (3) it then integrates these retrieved examples with the target’s calling context, employing a general-purpose LLM to synthesize high-fidelity, human-readable decompiled code. Evaluation on 3,200 functions from real-world software repositories demonstrates that BinRAG improves readability by 8.4% over standard RAG and semantic precision by 31.2% over the next-best prior baseline. Our results show that BinRAG effectively scales to large codebases and significantly reduces manual effort in reverse engineering tasks.

With the increasing development of Solidity contracts on Ethereum , more developers are reusing them on other compatible blockchains. However, developers may overlook the differences between the designs of the blockchain system, such as the Gas Mechanism and Consensus Protocol , leading to the same contracts on different blockchains not being able to achieve consistent execution as on Ethereum . This inconsistency reveals design flaws in reused contracts, exposing code smells that hinder code reusability, and we define this inconsistency as EVM-Inequivalent Code Smells . In this paper, we conducted an empirical study to reveal the causes and characteristics of EVM-Inequivalent Code Smells . To ensure the identified smells reflect real developer concerns, we analyzed 1,379 security audit reports, 823 bug bounty reports, and 326 Stack Overflow posts related to reused contracts on EVM-compatible blockchains, such as Binance Smart Chain (BSC) and Polygon . Using the Open Card Sorting method, we defined nine types of EVM-Inequivalent Code Smells . To enable efficient detection, we developed EquivSage , a tool that leverages the contextual understanding capabilities of large language models (LLMs) to guide slicing and static taint analysis via task-specific prompts. Symbolic execution is integrated to improve detection reliability. An analysis of 1,263,683 contracts across six EVM-compatible blockchains using EquivSage reveals that, on average, 15.45% contain at least one EVM-Inequivalent Code Smell , underscoring its widespread prevalence. Since 2024, code smells in reused contracts on Ethereum , Polygon , and Optimism have increased significantly. While not all instances lead to financial loss, high frequency and asset exposure highlight the risks inherent in contract reuse. Developers are encouraged to avoid copy-and-paste practices and to detect such smells proactively before reuse.

Smart contracts have attracted rapid development and widespread application. Due to the complexity of real-world smart contracts, it is error-prone to correctly enforce all intended functionalities in code implementations, resulting in unintended functional behaviors and security issues in practice. Code-comment inconsistency detection has emerged as an important solution to these issues, which leverages the redundant functional specifications in comments to detect code implementations that violate developers' intentions. However, existing inconsistency detection solutions are typically pattern-based and limited to fixed types of inconsistencies, which prevents them from detecting the diverse inconsistencies between real-world code implementations and casually written comments. To bridge the gap, this paper presents SmartComment, the first technique that combines LLMs with program analysis techniques for detecting code-comment inconsistencies in smart contracts. SmartComment introduces an LLM-driven workflow which simulates real-world interactions between code reviewers and developers to identify inconsistencies. It incorporates various program analysis techniques into the workflow, including comment propagation and code context extraction for generating input context for inconsistency detection, as well as program variant generation and differential analysis for inconsistency confirmation. Our evaluation results show that SmartComment detects 203 valid inconsistencies from a dataset of 1,000 real-world contracts with a precision of 79.9%, highlighting its effectiveness in detecting prevalent and diverse real-world inconsistencies. Compared to previous work, SmartComment achieves both higher precision and recall, detecting over 90% of inconsistencies that existing methods fail to identify. Furthermore, an ablation experiment demonstrates the effectiveness of incorporating program analysis techniques into SmartComment, improving the F1-score from 58.7% to 81.3%.

Visual deep learning (VDL) systems power safety-critical applications like autonomous driving and demand comprehensive testing, where generating diverse image mutations is crucial. Recently, multi-modal large language models (MLLMs) introduced instruction-driven methods, offering a unified paradigm of creating diverse and complex image mutations by describing them in text. Despite the promise, their mutation quality and applicability remain largely unexplored. We present the first study assessing MLLM's adequacy in VDL testing, focusing on the semantic validity of mutated images, their alignment with text instructions (prompts), and the faithfulness in preserving unchanged semantics. Through large-scale human studies and quantitative analysis, we identify MLLMs’ potential. Notably, while SoTA MLLMs (e.g., GPT-4V) struggle with editing already-appeared semantics in images (e.g., rotating objects), they enable novel “semantic-replacement” mutations (e.g., “dress a dog with clothes” ) that were infeasible previously. These mutations bring new semantics, effectively triggering unique VDL faults. Thus, MLLM-based mutations are a vital complement to traditional methods, not a replacement. Building on our findings, we develop new testing metrics tailored to MLLM-enabled mutations and summarize lessons for employing and designing MLLM mutation pipelines in different testing scenarios. Our evaluation framework and generated dataset form a reusable benchmark for future research.

Detecting vulnerabilities in smart contracts is vital for the security and reliability of decentralized apps. To facilitate vulnerability detection, contract codes, including bug patterns, are represented as heterogeneous graphs with various nodes and edges, like control-flow and function-call graphs. However, existing graph-learning techniques struggle with large, complex graphs. This article presents MANDO-LLM, a novel framework that combines heterogeneous graph transformers (HGTs) with large language models (LLMs) for detecting vulnerabilities in smart contracts represented as heterogeneous contract graphs built upon control-flow and call graphs. MANDO-LLM uses LLMs to capture code features from control-flow and call data, customizes HGTs to learn embeddings with specific node-edge meta relations, and employs classifiers for vulnerability detection in Solidity code at both contract and line levels. Our evaluation shows that MANDO-LLM significantly outperforms existing methods on real-world large-scale imbalanced datasets, with F1-score improvements from 0.59% to 80.72% at the contract level. It is also one of the first effective methods for identifying line-level vulnerabilities, with performance boosts ranging from 3.09% to over 95% across different vulnerability types. MANDO-LLM’s versatility allows easy retraining for various vulnerabilities without needing manually defined patterns.

The popularity of smartphones has led to the growth of mobile app markets, creating a need for enhanced transparency, global access, and secure downloading. This article introduces AGChain, a blockchain-based gateway that enables trustworthy app delegation within existing markets. AGChain ensures that markets can continue providing services while users benefit from permanent, distributed, and secure app delegation. During its development, we address two key challenges: significantly reducing smart contract gas costs and enabling fully distributed IPFS-based file storage. Additionally, we tackle three system issues related to security and sustainability. We have implemented a prototype of AGChain on Ethereum and Polygon blockchains, achieving effective security and decentralization with a minimal gas cost of around 0.0028 USD per app upload (no cost for app download). AGChain also exhibits reasonable performance with an average overhead of 12%.