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Paper Abstract and Keywords
Presentation 2026-03-04 09:50
LLM-Based Generation of Proof-of-Concept Code for Evading Sandbox Analysis
Hikaru Matsuzawa, Yukihiro Higashi, Nguyen Thi Van Anh, Rui Tanabe, Yin Minn Pa Pa, Katsunari Yoshioka (Yokohama National Univ.) ICSS2025-116
Abstract (in Japanese) (See Japanese page) 
(in English) Sandbox analysis is essential for understanding malware behavior, yet evasive malware can detect analysis environments and avoid observation. Although many defenses have been proposed, evaluating sandbox evasion resistance remains difficult due to the lack of comprehensive test samples.

We present a large-scale dataset of proof-of-concept (PoC) programs for assessing sandbox evasion resistance. Using large language models (LLMs), we unify and reimplement PoCs from existing tools, extend coverage by generating additional techniques guided by prior technique taxonomies, and produce multiple implementation variants per technique. To mitigate errors in LLM-generated code, we retain only PoCs that pass a three-stage validation pipeline: buildability, correct execution, and verification of evasion-related behavior.

We generated 4,508 PoCs with an OpenAI model and curated 1,520 validated PoCs. When tested on a commercial sandbox, 593 PoCs (39.0%) detected the environment as a sandbox, compared with 56 (22.8%) of 246 PoCs from existing tools under the same conditions. Our dataset is larger and more effective than tool-based PoC sets, enabling more rigorous evaluation of sandbox evasion resistance.
Keyword (in Japanese) (See Japanese page) 
(in English) Malware Dynamic Analysis / Sandbox Evasion / Large Language Model / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 381, ICSS2025-116, pp. 224-230, March 2026.
Paper # ICSS2025-116 
Date of Issue 2026-02-24 (ICSS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF ICSS2025-116

Conference Information
Committee ICSS IPSJ-SPT  
Conference Date 2026-03-03 - 2026-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Prefectural Museum & Art Museum 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Security, Trust, etc. 
Paper Information
Registration To ICSS 
Conference Code 2026-03-ICSS-SPT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) LLM-Based Generation of Proof-of-Concept Code for Evading Sandbox Analysis 
Sub Title (in English)  
Keyword(1) Malware Dynamic Analysis  
Keyword(2) Sandbox Evasion  
Keyword(3) Large Language Model  
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1st Author's Name Hikaru Matsuzawa  
1st Author's Affiliation Yokohama National University (Yokohama National Univ.)
2nd Author's Name Yukihiro Higashi  
2nd Author's Affiliation Yokohama National University (Yokohama National Univ.)
3rd Author's Name Nguyen Thi Van Anh  
3rd Author's Affiliation Yokohama National University (Yokohama National Univ.)
4th Author's Name Rui Tanabe  
4th Author's Affiliation Yokohama National University (Yokohama National Univ.)
5th Author's Name Yin Minn Pa Pa  
5th Author's Affiliation Yokohama National University (Yokohama National Univ.)
6th Author's Name Katsunari Yoshioka  
6th Author's Affiliation Yokohama National University (Yokohama National Univ.)
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Speaker Author-1 
Date Time 2026-03-04 09:50:00 
Presentation Time 20 minutes 
Registration for ICSS 
Paper # ICSS2025-116 
Volume (vol) vol.125 
Number (no) no.381 
Page pp.224-230 
#Pages 7 
Date of Issue 2026-02-24 (ICSS) 

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