A Systematic Study of Code Obfuscation Against LLM-based Vulnerability Detection
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Xiao, Li, Yue, Wu, Hao, Zhang, Yue, Zhang, Yechao, Xu, Fengyuan, Zhong, Sheng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Everything You Wanted to Know About LLM-based Vulnerability Detection But Were Afraid to Ask
by: Li, Yue, et al.
Published: (2025)
by: Li, Yue, et al.
Published: (2025)
Usability as a Weapon: Attacking the Safety of LLM-Based Code Generation via Usability Requirements
by: Li, Yue, et al.
Published: (2026)
by: Li, Yue, et al.
Published: (2026)
If LLMs Would Just Look: Simple Line-by-line Checking Improves Vulnerability Localization
by: Li, Yue, et al.
Published: (2024)
by: Li, Yue, et al.
Published: (2024)
Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets
by: Zhang, Yechao, et al.
Published: (2025)
by: Zhang, Yechao, et al.
Published: (2025)
When LLMs Copy to Think: Uncovering Copy-Guided Attacks in Reasoning LLMs
by: Li, Yue, et al.
Published: (2025)
by: Li, Yue, et al.
Published: (2025)
LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation
by: He, Ying, et al.
Published: (2024)
by: He, Ying, et al.
Published: (2024)
CoTDeceptor:Adversarial Code Obfuscation Against CoT-Enhanced LLM Code Agents
by: Li, Haoyang, et al.
Published: (2025)
by: Li, Haoyang, et al.
Published: (2025)
Good-Enough LLM Obfuscation (GELO)
by: Belikov, Anatoly, et al.
Published: (2026)
by: Belikov, Anatoly, et al.
Published: (2026)
Identifying Obfuscated Code through Graph-Based Semantic Analysis of Binary Code
by: Cohen, Roxane, et al.
Published: (2025)
by: Cohen, Roxane, et al.
Published: (2025)
Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection
by: Liang, Zhihong, et al.
Published: (2025)
by: Liang, Zhihong, et al.
Published: (2025)
Efficient but Vulnerable: Benchmarking and Defending LLM Batch Prompting Attack
by: Yue, Murong, et al.
Published: (2025)
by: Yue, Murong, et al.
Published: (2025)
Evaluating Line-level Localization Ability of Learning-based Code Vulnerability Detection Models
by: Pintore, Marco, et al.
Published: (2025)
by: Pintore, Marco, et al.
Published: (2025)
Vulnerability Detection in C/C++ Code with Deep Learning
by: Huang, Zhen, et al.
Published: (2024)
by: Huang, Zhen, et al.
Published: (2024)
LLM Security and Safety: Insights from Homotopy-Inspired Prompt Obfuscation
by: Lazo, Luis, et al.
Published: (2026)
by: Lazo, Luis, et al.
Published: (2026)
A Systematic Study of Model Extraction Attacks on Graph Foundation Models
by: Xu, Haoyan, et al.
Published: (2025)
by: Xu, Haoyan, et al.
Published: (2025)
SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
by: Shan, Zhengyang, et al.
Published: (2026)
by: Shan, Zhengyang, et al.
Published: (2026)
Command-line Obfuscation Detection using Small Language Models
by: Outrata, Vojtech, et al.
Published: (2024)
by: Outrata, Vojtech, et al.
Published: (2024)
Deep Learning-based Binary Analysis for Vulnerability Detection in x86-64 Machine Code
by: Petingola, Mitchell
Published: (2026)
by: Petingola, Mitchell
Published: (2026)
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability
by: Haldar, Rajdeep, et al.
Published: (2024)
by: Haldar, Rajdeep, et al.
Published: (2024)
LLM-based Vulnerability Discovery through the Lens of Code Metrics
by: Weissberg, Felix, et al.
Published: (2025)
by: Weissberg, Felix, et al.
Published: (2025)
Disassembling Obfuscated Executables with LLM
by: Rong, Huanyao, et al.
Published: (2024)
by: Rong, Huanyao, et al.
Published: (2024)
Learning-based Models for Vulnerability Detection: An Extensive Study
by: Ni, Chao, et al.
Published: (2024)
by: Ni, Chao, et al.
Published: (2024)
Multi-Faceted Studies on Data Poisoning can Advance LLM Development
by: He, Pengfei, et al.
Published: (2025)
by: He, Pengfei, et al.
Published: (2025)
On the Difficulty of Selecting Few-Shot Examples for Effective LLM-based Vulnerability Detection
by: Hannan, Md Abdul, et al.
Published: (2025)
by: Hannan, Md Abdul, et al.
Published: (2025)
Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection
by: Gabbireddy, Divyesh, et al.
Published: (2026)
by: Gabbireddy, Divyesh, et al.
Published: (2026)
Online Poisoning Attack Against Reinforcement Learning under Black-box Environments
by: Li, Jianhui, et al.
Published: (2024)
by: Li, Jianhui, et al.
Published: (2024)
Dissecting the Black Box: Circuit-Level Analysis of LLM Vulnerability Detection
by: Atiiq, Syafiq Al, et al.
Published: (2026)
by: Atiiq, Syafiq Al, et al.
Published: (2026)
VulStyle: A Multi-Modal Pre-Training for Code Stylometry-Augmented Vulnerability Detection
by: Biringa, Chidera, et al.
Published: (2026)
by: Biringa, Chidera, et al.
Published: (2026)
Prompt Obfuscation for Large Language Models
by: Pape, David, et al.
Published: (2024)
by: Pape, David, et al.
Published: (2024)
Code-Centric Detection of Vulnerability-Fixing Commits: A Unified Benchmark and Empirical Study
by: Loose, Nils, et al.
Published: (2026)
by: Loose, Nils, et al.
Published: (2026)
The Autonomy Tax: Defense Training Breaks LLM Agents
by: Li, Shawn, et al.
Published: (2026)
by: Li, Shawn, et al.
Published: (2026)
MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation
by: Sun, Guokai, et al.
Published: (2025)
by: Sun, Guokai, et al.
Published: (2025)
FreqMark: Frequency-Based Watermark for Sentence-Level Detection of LLM-Generated Text
by: Xu, Zhenyu, et al.
Published: (2024)
by: Xu, Zhenyu, et al.
Published: (2024)
Instruction Backdoor Attacks Against Customized LLMs
by: Zhang, Rui, et al.
Published: (2024)
by: Zhang, Rui, et al.
Published: (2024)
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems
by: Tafreshian, Benyamin, et al.
Published: (2025)
by: Tafreshian, Benyamin, et al.
Published: (2025)
Low Rank Comes with Low Security: Gradient Assembly Poisoning Attacks against Distributed LoRA-based LLM Systems
by: Dong, Yueyan, et al.
Published: (2026)
by: Dong, Yueyan, et al.
Published: (2026)
CLOAK: Contrastive Guidance for Latent Diffusion-Based Data Obfuscation
by: Yang, Xin, et al.
Published: (2025)
by: Yang, Xin, et al.
Published: (2025)
Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
by: Kalavasis, Alkis, et al.
Published: (2024)
by: Kalavasis, Alkis, et al.
Published: (2024)
BadMerging: Backdoor Attacks Against Model Merging
by: Zhang, Jinghuai, et al.
Published: (2024)
by: Zhang, Jinghuai, et al.
Published: (2024)
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
by: Kumar, Abhinav, et al.
Published: (2025)
by: Kumar, Abhinav, et al.
Published: (2025)
Similar Items
-
Everything You Wanted to Know About LLM-based Vulnerability Detection But Were Afraid to Ask
by: Li, Yue, et al.
Published: (2025) -
Usability as a Weapon: Attacking the Safety of LLM-Based Code Generation via Usability Requirements
by: Li, Yue, et al.
Published: (2026) -
If LLMs Would Just Look: Simple Line-by-line Checking Improves Vulnerability Localization
by: Li, Yue, et al.
Published: (2024) -
Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets
by: Zhang, Yechao, et al.
Published: (2025) -
When LLMs Copy to Think: Uncovering Copy-Guided Attacks in Reasoning LLMs
by: Li, Yue, et al.
Published: (2025)