arXiv subjects

Yukai Zhao

Publications and source records attributed to Yukai Zhao.

7 recordsLinked to original sources

arXiv2026

Ensemble Fuzzing with Dynamic Resource Scheduling and Multidimensional Seed Evaluation

Fuzzing is widely used for detecting bugs and vulnerabilities, with various techniques proposed to enhance its effectiveness. To combine the advantages of multiple technologies, researchers proposed ensemble fuzzing, which integrates multiple base fuzzers. Despite promising results, state-of-the-art ensemble fuzzing techniques face limitations in resource scheduling and performance evaluation, leading to unnecessary resource waste. In this paper, we propose Legion, a novel ensemble fuzzing framework that dynamically schedules resources during the ensemble fuzzing campaign. We designed a novel resource scheduling algorithm based on the upper confidence bound algorithm to reduce the resource consumption of ineffective base fuzzers. Additionally, we introduce a multidimensional seed evaluation strategy, which considers multiple metrics to achieve more comprehensive fine-grained performance evaluation. We implemented Legion as a prototype tool and evaluated its effectiveness on Google's fuzzer-test-suite as well as real-world open-source projects. Results show that Legion outperforms existing state-of-the-art base fuzzers and ensemble fuzzing techniques, detecting 20 bugs in real-world open-source projects-five previously unknown and three classified as CVEs.

cs.SE↗

arXiv2026

Mitigating Package Hallucinations in Large Language Models via Model Editing

Large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, such as code generation, library recommendation, and dependency configuration. However, recent studies show that LLMs may suffer from package hallucination, where they generate non-existent or invalid package names. These hallucinations can be exploited in software supply chain attacks, as attackers may register malicious packages under hallucinated names. Therefore, mitigating package hallucination is important for improving the reliability and security of LLM-assisted software development. In this paper, we introduce BOUND, a lightweight localized model editing framework for mitigating package hallucinations in LLMs. BOUND formulates package hallucination mitigation as a package-validity boundary editing problem, where the boundary refers to the model's ability to distinguish valid packages from hallucinated package names under a given task context. It first locates modules related to package hallucination through a risk-aware localization strategy, and then edits these modules with lightweight LoRA adapters using a boundary-aware objective that reinforces valid packages, suppresses hallucinated packages, and preserves locality behavior. Experimental results show that BOUND effectively reduces package hallucinations while preserving valid package recommendations. In the package recommendation task, BOUND reduces package-level hallucination rate (Package-HR) by 79.9% on edit prompts and by 65.4% on unseen prompts. The learned package-validity boundary further generalizes to other package-related tasks, reducing Package-HR by 12.8% in code generation and by 34.0% in pip install recommendation. These results show that BOUND refines the package-validity boundary of LLMs and improves the reliability of package-related outputs.

cs.SE↗

arXiv2026

Exploiting LLM Agent Supply Chains via Payload-less Skills

Autonomous agents powered by Large Language Models (LLMs) acquire external functionalities through third-party skills available in open marketplaces. Adopting these integrations broadens the potential attack surface, prompting a need for systematic security evaluation. Current auditing mechanisms are effective at identifying explicit code payloads and predefined threat contents through security scanning. These detection mechanisms are bypassed if malicious behaviors lack direct injection and are instead synthesized dynamically at runtime through the agent's inherent generative capabilities. Exploring this blind spot, we introduce Semantic Compliance Hijacking (SCH), a payload-less supply chain attack targeting autonomous coding environments. The SCH approach translates malicious goals into unstructured natural language instructions formatted as necessary compliance rules, leading the agent to generate and execute unauthorized code. To assess the real-world viability of this attack, we developed an automated pipeline to evaluate its effectiveness across a test matrix comprising three mainstream agent frameworks and three distinct foundation models using contextualized scenarios. The findings demonstrate the pervasive nature of this threat, with SCH achieving peak success rates of up to 77.67% for confidentiality breaches and 67.33% for Remote Code Execution (RCE) under the most vulnerable configurations. Furthermore, the introduction of Multi-Skill Automated Optimization (MS-AO) further boosted attack efficacy. By omitting recognizable Abstract Syntax Tree (AST) signatures and explicit harmful intents, the manipulated skill files maintained a 0.00% detection rate, evading current scanning tools. This research highlights an underexplored attack surface within agent supply chains, pointing to a necessary transition from signature-based detection models toward semantic intent validation.

cs.CR↗

arXiv2026

Triggering and Detecting Exploitable Library Vulnerability from the Client by Directed Greybox Fuzzing

Developers utilize third-party libraries to improve productivity, which also introduces potential security risks. Existing approaches generate tests for public functions to trigger library vulnerabilities from client programs, yet they depend on proof-of-concepts (PoCs), which are often unavailable. In this paper, we propose a new approach, LiveFuzz, based on directed greybox fuzzing (DGF) to detect the exploitability of library vulnerabilities from client programs without PoCs. LiveFuzz exploits a target tuple to extend existing DGF techniques to cross-program scenarios. Based on the target tuple, LiveFuzz introduces a novel Abstract Path Mapping mechanism to project execution paths, mitigating the preference for shorter paths. LiveFuzz also proposes a risk-based adaptive mutation to mitigate excessive mutation. To evaluate LiveFuzz, we construct a new dataset including 61 cases of library vulnerabilities exploited from client programs. Results show that LiveFuzz increases the number of target-reachable paths compared with all baselines and improves the average speed of vulnerability exposure. Three vulnerabilities are triggered exclusively by LiveFuzz.

cs.CR↗

arXiv2025

HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing

Large Language Models (LLMs) are widely used for code generation, but they face critical security risks when applied to practical production due to package hallucinations, in which LLMs recommend non-existent packages. These hallucinations can be exploited in software supply chain attacks, where malicious attackers exploit them to register harmful packages. It is critical to test LLMs for package hallucinations to mitigate package hallucinations and defend against potential attacks. Although researchers have proposed testing frameworks for fact-conflicting hallucinations in natural language generation, there is a lack of research on package hallucinations. To fill this gap, we propose HFUZZER, a novel phrase-based fuzzing framework to test LLMs for package hallucinations. HFUZZER adopts fuzzing technology and guides the model to infer a wider range of reasonable information based on phrases, thereby generating enough and diverse coding tasks. Furthermore, HFUZZER extracts phrases from package information or coding tasks to ensure the relevance of phrases and code, thereby improving the relevance of generated tasks and code. We evaluate HFUZZER on multiple LLMs and find that it triggers package hallucinations across all selected models. Compared to the mutational fuzzing framework, HFUZZER identifies 2.60x more unique hallucinated packages and generates more diverse tasks. Additionally, when testing the model GPT-4o, HFUZZER finds 46 unique hallucinated packages. Further analysis reveals that for GPT-4o, LLMs exhibit package hallucinations not only during code generation but also when assisting with environment configuration.

cs.SE↗

arXiv2024

Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting

Encoding time series into tokens and using language models for processing has been shown to substantially augment the models' ability to generalize to unseen tasks. However, existing language models for time series forecasting encounter several obstacles, including aliasing distortion and prolonged inference times, primarily due to the limitations of quantization processes and the computational demands of large models. This paper introduces Apollo-Forecast, a novel framework that tackles these challenges with two key innovations: the Anti-Aliasing Quantization Module (AAQM) and the Race Decoding (RD) technique. AAQM adeptly encodes sequences into tokens while mitigating high-frequency noise in the original signals, thus enhancing both signal fidelity and overall quantization efficiency. RD employs a draft model to enable parallel processing and results integration, which markedly accelerates the inference speed for long-term predictions, particularly in large-scale models. Extensive experiments on various real-world datasets show that Apollo-Forecast outperforms state-of-the-art methods by 35.41\% and 18.99\% in WQL and MASE metrics, respectively, in zero-shot scenarios. Furthermore, our method achieves a 1.9X-2.7X acceleration in inference speed over baseline methods.

cs.LG↗

arXiv2021

Automated Allocation of Detention Rooms Based on Inverse Graph Partitioning

Room allocation is a challenging task in detention centers since lots of related people need to be held separately with limited rooms. It is extremely difficult and risky to allocate rooms manually, especially for organized crime groups with close connections. To tackle this problem, we develop an intelligent room allocation system for detention centers to provide optimized room allocation schemes automatically. We first formalize the detention room allocation problem as inverse graph partitioning, which can measure the quality of room allocation schemes. Then, we propose two heuristic algorithms to achieve the global optimization and local optimization of detention room allocation. Experiment results on real-world datasets show that the proposed algorithms significantly outperform manual allocation and suggest that the system is of great practical application value.

cs.SI↗