Highlights
The Korean hacking team “0x4b52,” which included Professor Insu Yun of the School of Electrical Engineering as well as numerous KAIST students and alumni, finished second at DEF CON CTF 2026, held in Las Vegas, the United States, from August 7 to 9.
Comprising approximately 30 Korean hackers, 0x4b52 competed against world-class multinational teams to claim the runner-up position. This marks the best result achieved by an all-Korean team at DEF CON CTF since the Korean team “DEFK0R” won the competition in 2015.
Of the 686 teams that participated in the qualifiers, only the top 12 advanced to the finals. After qualifying in fifth place, 0x4b52 climbed the rankings to finish second overall. The achievement is particularly significant because KAIST members from different generations, including undergraduate and graduate students as well as alumni, competed together as one team against the world’s leading hackers.
“I find it deeply meaningful that our students and alumni demonstrated their outstanding capabilities on a stage where the world’s most accomplished hackers compete,” said KAIST President Choongsik Bae. “This achievement is even more significant because it was accomplished through a joint effort by multiple generations of KAIST members, from undergraduate and graduate students to alumni.”
President Bae added, “In the era of AI, a strong foundation and domain expertise are more important than ever. KAIST will actively support students in challenging themselves in areas of interest from the undergraduate level, building expertise through hands-on experience and research, and growing into global talents who can take the lead in leveraging AI and forge new paths.”
DEF CON CTF is a flagship event of DEF CON, one of the world’s largest hacker conferences. It is an international hacking competition in which elite hackers from around the globe test their capabilities in system security and hacking. This year’s finals were held as one of the main events of DEF CON 34 in Las Vegas. The multinational team “Blue Water” won the championship, while the Korean team 0x4b52 finished second.
Capture The Flag (CTF) is a type of hacking competition in which teams earn points by analyzing vulnerabilities in given systems and software and conducting offensive and defensive operations. Participants must analyze systems and software, identify vulnerabilities, and compete against opposing teams within a limited time. The competition therefore requires advanced technical expertise as well as close teamwork and strategic judgment.
This year’s qualifiers featured challenges across a wide range of information security fields, including binary exploitation, reverse engineering, cryptography, and web exploitation.
0x4b52 is a Korean team formed primarily by members of the hacking team from Korea, HypeBoy, and Professor Insu Yun’s laboratory at KAIST. Approximately one-third of the team’s roughly 30 members are currently affiliated with or previously worked in Professor Yun’s Hacking Lab.
Numerous other KAIST students and alumni also participated. Many began exploring system hacking and security research as undergraduates, including through the information security club “GoN.” They further developed their expertise through coursework, research, and other opportunities in the School of Electrical Engineering, the School of Computing, and the Graduate School of Information Security. Undergraduate and master’s and doctoral students competed on the same team alongside alumni now working in industry and research institutions, bringing together different generations of KAIST hackers.
Professor Yun’s research team, one of the central groups within 0x4b52, also reached the top of a major global cybersecurity competition in Las Vegas last year. Together with researchers from Samsung Research, POSTECH, and the Georgia Institute of Technology, Professor Yun’s team formed “Team Atlanta” and participated in the AI Cyber Challenge (AIxCC), organized by the U.S. Defense Advanced Research Projects Agency (DARPA). The team won the final round held at DEF CON 33 in August 2025.
AIxCC is a competition focused on technologies that use artificial intelligence to automatically detect and repair software vulnerabilities. Team Atlanta received a prize of USD 4 million for winning the finals. At this year’s DEF CON CTF, Professor Yun’s research team competed alongside KAIST students and alumni against some of the world’s leading teams in system security. This follows the team’s victory last year in a global competition for AI-powered autonomous cyber defense technology, marking back-to-back achievements on the international stage.
The achievement also highlights KAIST’s approach to developing information security talent—giving students hands-on hacking experience as undergraduates, supporting them as they pursue specialized security research in graduate school, and helping them build professional expertise after graduation.
At KAIST, undergraduate students gain hands-on experience analyzing real-world systems and identifying vulnerabilities through coursework and student clubs. At the graduate level, students pursue specialized system security research in areas such as software vulnerability analysis, program analysis, and automated vulnerability detection. Meanwhile, KAIST is expanding its education and research capacity in information security by training master’s- and doctoral-level specialists and participating in the government-supported Information Security Specialized University Program.
“This achievement reflects the collective effort of students who have learned from one another, gained hands-on experience, and grown together over many years,” said Professor Insu Yun. “I also became deeply involved in information security through GoN as an undergraduate at KAIST. That makes it particularly meaningful to see students and alumni from different generations come together, compete as a team against some of the world’s best hackers, and achieve this result.”
Dr. Woojun Kim, who completed his doctoral research under the supervision of Professor Youngchul Sung in our department, will join the Department of Industrial and Systems Engineering at KAIST as an Assistant Professor, effective August 2026. Dr. Kim earned both his M.S. and Ph.D. degrees from the School of Electrical Engineering at KAIST and subsequently served as a Postdoctoral Researcher at Carnegie Mellon University’s Robotics Institute, where he conducted research on multi-agent systems, reinforcement learning, and robotics.
Dr. Kim’s research focuses on developing learning methodologies that enable multiple intelligent agents and robots to collaborate and make effective decisions in real-world environments, with an emphasis on scalability, robustness, safety, and fairness. He has published his work in top-tier artificial intelligence conferences, including NeurIPS, ICML, ICLR, and AAMAS, as well as leading robotics venues such as ICRA and CoRL. His research excellence has been recognized with a NeurIPS Spotlight paper, the AAMAS Best Paper Award Finalist, the ICRA Best Conference Paper Finalist and Best Multi-Robot Systems Paper Finalist, and the Best Paper Award at the RSS 2025 GenAI-HRI Workshop.
In his new position at KAIST, Dr. Kim will continue to advance his research on intelligent agent and robotic collaboration powered by multi-agent systems and reinforcement learning. We wish him great success in his research and teaching endeavors.
Professor Hyun Myung’s research team (Urban Robotics Lab) from our department secured second place in the NaviTrace Challenge, held during the Open-World Navigation (OWN) Workshop at RSS 2026 (Robotics: Science and Systems) in Sydney, Australia, from July 13 to 17. The international competition evaluated an AI’s ability to infer navigation paths on a given image based solely on a single first-person perspective photograph and a brief natural language instruction.
Commanding a robot to “cross the street” provides no explicit information about finding a crosswalk or waiting for a signal; the robot must independently decipher these implicit social norms from the scene before it. However, assigning goal identification, hazard assessment, and path generation to a single AI model leads to task interference and degraded performance. To address this, the research team developed PRISM-Nav, which divides the process among four specialized agents. Three agents simultaneously identify target points, hazardous elements such as stairs or curbs, and socially relevant structures like crosswalks or sidewalks, while a final agent synthesizes their outputs to generate the path. Crucially, the agents communicate by drawing symbols directly onto the input photograph rather than exchanging text or coordinates, significantly reducing spatial information loss—much like marking a location on a map instead of describing it verbally. Because it requires no additional training, PRISM-Nav can be immediately deployed to new robots or environments.
The team scored 53 points, placing second behind the joint team from Nanjing University and FiveAges. A key highlight of the achievement was the economic efficiency of the underlying AI models. While the top-performing general-purpose model provided as a reference by the organizers (Gemini 3.1 Pro Preview) carries high operational costs that make continuous deployment on real robots difficult, the team achieved performance surpassing that benchmark using a far more affordable lightweight model (Gemini 3 Flash). This demonstrates that deploying multiple low-cost agents offers a clear advantage in both cost and performance over relying on a single expensive model.
Professor Myung noted, “This result proves that large AI models can be effectively utilized for real-world decision-making in robotics without requiring additional training data. It will serve as a foundational technology for service robots, such as delivery and guide robots, that share physical spaces with humans.”
Meanwhile, Professor Myung’s research group previously secured first place in international challenges at the ICRA 2026 and CVPR 2026 workshops last June. The laboratory continues to expand its expertise in spatial perception toward the field of Embodied AI.
AI semiconductors are becoming more programmable. KAIST researchers have developed a device whose response characteristics can be programmed to process data changing at different speeds. The technology reduced prediction errors for time-varying data by up to 40-fold and is expected to enhance real-time AI performance in autonomous vehicles, robots, and wearable devices.
A research team led by Chair Professor Shinhyun Choi from the School of Electrical Engineering and the Graduate School of Semiconductor Technology has developed a programmable dynamic memtransistor (PDM), a semiconductor device whose time-response characteristics can be adjusted to multiple states and retained, as well as an integrated array based on the device.
A memtransistor is a next-generation semiconductor device that combines the information-storage function of memory with the computing function of a transistor. In the developed PDM, the ability to process data while retaining previous information allows its response characteristics to be adjusted and retained for incoming data.
Today’s computers and smartphones require complex software processing to analyze data that changes over time, resulting in large computational loads and high power consumption. To address this, researchers have been studying technologies that allow semiconductor hardware itself to process data directly. However, conventional devices have had fixed response speeds that cannot be changed once the device is fabricated.
The research team overcame this limitation by introducing a dual-layer structure inside the transistor, combining a charge storage layer that accumulates and processes data with an electron trapping layer that controls the response speed in a nonvolatile manner.
In the PDM developed by the research team, incoming data is processed in the charge storage layer, while the electron trapping layer controls, across multiple levels, the recovery speed at which the semiconductor returns to its original state. In experiments, the team succeeded in tuning the current recovery time over an approximately 5-fold range and the characteristic frequency over a range of more than 10-fold.
In particular, in experiments involving the prediction of data in which fast and slow changes are intricately mixed, the PDM reduced prediction errors by as much as 40 times compared with conventional fixed-response semiconductor devices. The PDM enables accurate information processing even when handwriting or object-movement speeds vary, by using response characteristics configured to match different input timescales. Once the response characteristics are set, the device remembers them without requiring a continuous external power supply, and it does not require complex preprocessing of input data. Because it is fully compatible with materials used in widely adopted commercial semiconductor processes, it is also highly advantageous for mass production and commercialization.
The research team fabricated a PDM array and used it to predict complex data, confirming that it achieved accuracy comparable to conventional software-based systems while consuming far less energy.
“This study demonstrates an AI semiconductor whose response characteristics can be programmed to efficiently process data changing at different speeds,” said Chair Professor Choi. “We expect it to become a core technology that improves the performance of AI devices such as autonomous vehicles, robots, and wearables while reducing their power consumption.”
This research was led by KAIST Graduate School of Semiconductor Technology Ph.D. candidate Dae-won Kim as the first author, with Yoonho Cho, Seokho Seo, Yujin Kim, See-On Park, Taehwan Jang, and Chaebin Park participating as co-authors. Young Taek Oh and Fellow Jae-Duk Lee of Samsung Electronics’ Semiconductor R&D Center also participated as co-authors, and Chair Professor Shinhyun Choi served as the corresponding author. The research was published in July in the internationally renowned journal Nature Communications on July 4.
- Paper title: Programmable memtransistor array with temporal dynamics modulation for efficient time-series data processing
- DOI: https://doi.org/10.1038/s41467-026-75211-5
This research was supported by the National R&D Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT, the ETRI R&D Support Program of the Institute of Information & Communications Technology Planning & Evaluation, the HRD Program for Industrial Innovation of the Korea Institute for Advancement of Technology funded by the Ministry of Trade, Industry and Energy, Samsung Electronics, and others.
Professor Chang D. Yoo’s research team at the School of Electrical Engineering, KAIST, has been selected for the AX Entrepreneurial Talent Development Center, a national AI and Digital Startup Talent Development Program supported by the Ministry of Science and ICT (MSIT) and the Institute of Information & Communications Technology Planning & Evaluation (IITP). In conjunction with the project, KAIST has officially launched the AX DeepTech Bridge Center, a new platform designed to accelerate AI-driven deep technology innovation and entrepreneurship. Led by KAIST in collaboration with POSTECH, Seoul National University Bundang Hospital, and Jeonbuk National University, the program will be conducted over a period of five and a half years, from July 2026 through December 2031.
As Artificial Intelligence expands beyond digital services into every sector of society, the era of AI Transformation (AX) demands a new generation of innovators who can combine frontier AI technologies with deep domain expertise to solve real-world challenges. While significant advances have been made in AI research, existing educational and innovation ecosystems often separate research, technology transfer, and entrepreneurship, limiting the creation of globally competitive DeepTech startups.
To address this challenge, the research team has established the AX DeepTech Bridge Center, built upon the KAIST AI Hub, to integrate cutting-edge AI algorithms, foundation models, and computing technologies with domain expertise spanning semiconductors, energy systems, healthcare, sensors, and other strategic industries. Rather than focusing solely on research excellence, the Center provides a comprehensive innovation pipeline that connects education, collaborative research, technology validation, intellectual property, technology transfer, startup incubation, and venture investment into a unified ecosystem.
The AX DeepTech Bridge Center is founded on the principle of “AI Hub × Domain Expertise × Entrepreneurship.” By bringing together world-class AI researchers and leading domain experts, the Center will cultivate entrepreneurial graduate students capable of translating scientific breakthroughs into globally competitive DeepTech ventures. Leveraging KAIST’s AI Hub, Software Education Center, Office of Technology Commercialization, and Hwasung Science Hub, the initiative establishes one of Korea’s first fully integrated ecosystems that supports the entire journey from advanced AI research to commercialization and startup creation.
Through the AX Entrepreneurial Talent Development Center and the launch of the AX DeepTech Bridge Center, KAIST aims not only to nurture the next generation of AI entrepreneurs but also to establish a new model for AI-driven innovation that bridges fundamental research, industrial transformation, and technology entrepreneurship. The initiative is expected to play a pivotal role in strengthening Korea’s global competitiveness in DeepTech innovation while accelerating AI transformation across strategic industries.
Yong Man Ro, corresponding author); Youngjoon Yu (co-first author of the DNA study) >
Multimodal large language models (MLLMs), which process multiple types of sensory information such as text, images, and audio at the same time, are rapidly expanding the range of applications for artificial intelligence (AI). However, in real-world environments, these models can misinterpret the physical characteristics of sensors, mistakenly identify objects, or claim to hear sounds that are not actually present simply because a certain object appears in a video. These errors are known as hallucinations. A KAIST research team has developed a new technology that corrects such information confusion and physical misperceptions in AI.
A research team led by Professor Yong Man Ro from the School of Electrical Engineering has developed two core technologies that overcome the tendency of existing large language models to rely too heavily on ordinary camera (RGB) images and enable AI to suppress cross-modal hallucinations that occur when different sensory inputs become mixed.
The first technology developed by the research team is the Diverse Negative Attributes (DNA) optimization method, which helps AI accurately understand the physical characteristics of special camera sensors such as thermal, depth, and X-ray sensors. Existing AI models often failed to understand the physical meaning of such images, for example by mistaking bright areas in thermal images for simple light reflection.
The research team built VS-TDX, the first comprehensive benchmark for evaluating diverse vision sensors, and used the types of wrong answers that AI frequently produces as learning signals to help the model internalize the characteristics of each sensor. As a result, the AI gained a “new eye” that allows it to accurately infer the state of objects even in darkness or smoke.
The second technology is Modality-Adaptive Decoding (MAD), a control method that blocks hallucinations caused by confusion between visual and auditory information at the source. This technology prevents AI from mistakenly claiming that it hears a sound that does not actually exist simply because a certain object appears in a video.
MAD works by having the AI self-assess whether vision or audio is more important for a given task, and then increasing the weight of the more relevant modality in real time. A key advantage of this technology is that it can immediately suppress hallucination errors without costly model retraining, as it is training-free.
Instead of retraining AI models at large scale with massive computing resources, the research team maximized cost efficiency by introducing the DNA method, which enables fine adjustment with only a small amount of data, and the MAD plug-in approach, which requires no additional training at all.
These technologies can be applied to autonomous vehicles operating at night or in bad weather, robots performing missions in smoke-filled environments, and unmanned aerial vehicles using thermal cameras. They are also expected to be useful in fields that process multiple types of sensor information together, such as airport X-ray security screening and medical image analysis.
Professor Yong Man Ro said, “This research is significant because it reduces AI’s sensory bias and misperceptions without large-scale retraining,” adding, “It will serve as a foundation for building multimodal AI that can be trusted in real-life and industrial settings.”
This achievement was notable for its continuity, with Sangyun Chung, a doctoral student in KAIST’s School of Electrical Engineering, participating as first author in both studies. Dr. Youngjun Yoo also participated as co-first author in the DNA study.
Among the related papers, the MAD study was presented in June at the Conference on Computer Vision and Pattern Recognition (CVPR), the world’s leading international conference in AI and computer vision. The DNA study was published in IEEE Transactions on Image Processing, a leading international journal in the field of image processing.
- Paper title: Enhanced Vision-Language Models for Diverse Sensor Understanding: Cost-Efficient Optimization and Benchmarking
- DOI: 10.48550/arXiv.2412.20750 Author information: Sangyun Chung (KAIST, co-first author), Youngjun Yoo (KAIST, co-first author), Se Yeon Kim (KAIST, third author), Youngchae Chee (KAIST, fourth author), Yong Man Ro (KAIST, corresponding author)
- Paper title: MAD: Modality-Adaptive Decoding for Mitigating Cross-Modal Hallucinations in Multimodal Large Language Models,
- DOI: 10.48550/arXiv.2601.21181
- Author information: Sangyun Chung (KAIST, first author), Se Yeon Kim (KAIST, second author), Youngchae Chee (KAIST, third author), Yong Man Ro (KAIST, corresponding author)
- Related demo video: https://youtu.be/VuP9i6Vfk8o
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This research was supported by the Institute of Information & Communications Technology Planning & Evaluation’s (IITP’s) Human-Centered AI Core Technology Development Program and by a Center for Applied Research in Artificial Intelligence (CARAI) grant funded by the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD).
Dr. Sungjun Ahn, an alumnus of the Advanced Radio Technology (ART) Lab in the KAIST School of Electrical Engineering (Advisor: Prof. Joonhyuk Kang), has been appointed as a tenure-track faculty member in the Department of Artificial Intelligence and Information Technology at Sejong University, effective September 1, 2026.
Dr. Ahn received his B.S., M.S., and Ph.D. degrees from the School of Electrical Engineering, KAIST. He has been with the Media Research Division, Electronics and Telecommunications Research Institute (ETRI) since 2017, where he has served as a Senior Research Engineer and Research Fellow Board Member. His work has focused on digital terrestrial broadcasting systems, broadcast-broadband convergence, semantic media, and terrestrial time-transfer technologies. His doctoral dissertation, “On the Convergence and Applications of Broadcast and Broadband: System and Network Design Perspectives,” received the Outstanding Ph.D. Dissertation Award from the KAIST College of Engineering.
Dr. Ahn has made substantial contributions to the development and international standardization of ATSC 3.0 physical-layer technologies. In particular, he served as a principal drafter of the ATSC 3.0 MIMO Extension amendment and contributed to the world’s first commercial deployment of terrestrial MIMO broadcasting technology. He also played a leading role in the physical-layer standardization of Brazil’s new terrestrial broadcasting standard, DTV+, resulting in the final adoption of the ATSC 3.0 MIMO Extension as Brazil’s national physical-layer standard.
For his research and international standardization contributions, Dr. Ahn received the ETRI 50th Anniversary Outstanding Researcher Award as its representative recipient, the Grand Prize of the ETRI Foundational Patent Award, the ETRI Young Researcher Award, the High-Impact International Standard Technology Award, and a Commendation from the Minister of Science and ICT. In 2025, he was selected as an ETRI Next-Generation Leading Young Researcher.
Since 2022, Dr. Ahn has also served as an Associate Editor of IEEE Transactions on Broadcasting, a leading international journal in broadcasting and multimedia. He has authored more than 100 technical publications and patents.
Moving forward, Dr. Ahn will pursue research at the intersection of wireless communications, media, artificial intelligence, and information theory, with an emphasis on connecting theories with physical systems, practical applications, and international standards.
A research team led by Professor David Hyunchul Shim from the School of Electrical Engineering at KAIST has successfully conducted a field demonstration of its humanoid robot, ‘PIBOT,’ operating as a naval helmsman in collaboration with the Republic of Korea (ROK) Navy, proving the applicability of AI and robotics technologies in the defense sector.
This demonstration marks a significant milestone as the military’s first ‘robot crew experiment on a real naval environment,’ showing that a humanoid robot can directly replace the physical tasks of human crew members without any modifications or upgrades to existing vessel systems.
The research team integrated Large Language Model (LLM) technology—capable of understanding context and generating appropriate responses—into PIBOT. This design enables the robot to autonomously recognize and execute complex ship maneuvering procedures and verbal commands. Also, the robot controls the ship to follow the verbally commanded heading as it monitors the ship’s motion in real time.
On July 23, during the Stage 1 land-based maneuvering test at the ROK Navy Education and Training Command’s maneuvering simulator, PIBOT received voice commands from the bridge officer on watch such as “Rudder left 5 degrees, steady on 330 degrees!” PIBOT accurately recognized and repeated the command before manually operating the helm. Once the vessel reached the target heading, PIBOT successfully delivered a completion report, stating, “Steady on 330 degrees completed!”
The one-hour test simulated various complex and harsh navigation conditions, including navigating narrow waterways with heavy ship traffic, harsh offshore weather, and nighttime navigation. The research team plans to thoroughly analyze response times from command input to execution, steering precision, and operator convenience data to further enhance PIBOT’s control algorithms.
Building on the success of this Stage 1 land simulation, Professor Shim’s team plans to gradually expand the scope of verification to tests on ground test vessel (Stage 2), daytime navigation at sea (Stage 3), and long-term day and night navigation at sea (Stage 4).
“This research serves as a crucial stage to demonstrate how physical AI humanoid robots developed in our school can perform high-stake military missions amid the complex global situations environments and also severely shrinking military manpower in our country,” said Professor Shim. “Starting with ship maneuvering, we will expand the capability of the helmsman robot robots to cover broader functionalities.”
The development of PIBOT by Professor Shim’s research team is part of the Future Challenge Defense Technology R&D Project supported by the Agency for Defense Development (ADD), funded with 5.7 billion KRW from the ROK Defense Acquisition Program Administration (DAPA) since 2022. It is also jointly carried by our School’s Prof. Min Jun Kim’s team.
