arXiv subjects

Miseon Yu

Publications and source records attributed to Miseon Yu.

3 recordsLinked to original sources

arXiv2026

MACGen: Toward Functionally Correct and Secure Code Generation via Multi-Agent Collaboration

Despite their strong ability to generate code, large language models often fail to produce secure code, as their outputs frequently contain security vulnerabilities. Secure code generation is inherently challenging because it requires solving a multi-objective problem: functional correctness and security. Existing approaches address this challenge by injecting external security knowledge or by using agentic feedback and iterative refinement. However, guideline retrieval often leaves the generator to translate generic advice into task-specific secure implementations, while shared-dialogue multi-agent feedback can blur role boundaries and suffer from context bloat. We present MACGen, a multi-agent framework that integrates planning, security analysis, code synthesis and refinement to jointly optimize security and functionality. A planner constructs a step-by-step plan to satisfy functional requirements. A security advisor identifies likely CWEs and synthesizes task-specific guidelines, a coder then generates code grounded in these artifacts, and a reviewer issues perspective-separated feedback. Rather than sharing full dialogue histories, each agent receives only structured artifacts from upstream stages, enforcing role specialization and reducing uncontrolled context growth. On CWEval and BaxBench, MACGen improves F&S@1 over direct prompting by 19.61 and 10.57 percentage points (pp) on average, respectively.

cs.CR↗

arXiv2025

Rethinking the Capability of Fine-Tuned Language Models for Automated Vulnerability Repair

Learning-based automated vulnerability repair (AVR) techniques that utilize fine-tuned language models have shown promise in generating vulnerability patches. However, questions remain about their ability to repair unseen vulnerabilities. Our empirical study reveals that state-of-the-art models often overfit to the training set and are evaluated using training, validation, and test sets that are not mutually exclusive. Furthermore, relying on match-based metrics that compare generated patches to reference fixes at the token level has some limitations, failing to account for the possibility of various valid ways to patch the vulnerability. In this paper, we examine the capabilities of state-of-the-art fine-tuned AVR models and the adequacy of match-based evaluation metrics in three ways. First, we apply semantic-preserving transformations to test sets in order to determine whether models truly learn robust vulnerability-repair patterns or simply rely on spurious features. Second, we re-split the training, validation, and test sets to be mutually exclusive and evaluate the models on the revised test set to assess their generalization capabilities. Third, we introduce L-AVRBench, a test-based benchmark tailored for learning-based AVR, to overcome the limitations of match-based metrics and examine the AVR models' true repair capabilities.

cs.SE↗

arXiv2024

VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification

Vertical Federated Learning (VFL) focuses on handling vertically partitioned data over FL participants. Recent studies have discovered a significant vulnerability in VFL to backdoor attacks which specifically target the distinct characteristics of VFL. Therefore, these attacks may neutralize existing defense mechanisms designed primarily for Horizontal Federated Learning (HFL) and deep neural networks. In this paper, we present the first backdoor defense, called VFLIP, specialized for VFL. VFLIP employs the identification and purification techniques that operate at the inference stage, consequently improving the robustness against backdoor attacks to a great extent. VFLIP first identifies backdoor-triggered embeddings by adopting a participant-wise anomaly detection approach. Subsequently, VFLIP conducts purification which removes the embeddings identified as malicious and reconstructs all the embeddings based on the remaining embeddings. We conduct extensive experiments on CIFAR10, CINIC10, Imagenette, NUS-WIDE, and BankMarketing to demonstrate that VFLIP can effectively mitigate backdoor attacks in VFL. https://github.com/blingcho/VFLIP-esorics24

cs.LG↗