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KAIST Develops Smartphone-Based Technology to Detect Hidden Cameras A smartphone can now be transformed into a “hidden-camera detector.” KAIST researchers have developed an AI technology that can detect hidden cameras using only a smartphone and a low-cost LED device. This new security technology enables users to protect their privacy more easily and is expected to help prevent illegal filming in everyday spaces such as hotels and short-term rentals. KAIST (President Choongsik Bae) announced on August 30 that a research team led by Professor Jun Han of the School of Computing, in collaboration with the National University of Singapore and Singapore Management University, has developed “SweepLED,” a technology that detects hidden cameras by attaching an LED case to a smartphone. As hidden cameras are increasingly being installed in everyday spaces such as hotels, short-term rentals, and restrooms, the need is growing for detection technology that everyday users can easily use. However, existing portable detectors require users to visually identify bright reflective spots, which can lead to false positives by mistaking reflections from metal, glass, or glossy plastic surfaces for camera lenses. SweepLED works by keeping the smartphone camera fixed while changing only the direction of the LED illumination, then analyzing the patterns of reflected light that appear on object surfaces. Reflections from ordinary glossy objects tend to move or disappear depending on the direction of the light. In contrast, camera lenses show distinctive deformation patterns in their reflections due to their internal lens, aperture, and sensor structures. The research team uses deep learning-based analysis to distinguish these differences in temporal reflection patterns. While conventional detection methods rely on the user’s eyes to simply look for “bright spots,” SweepLED is different in that it analyzes both the movement and shape changes of reflections across multiple lighting angles. This enables more reliable detection of hidden camera lenses inside various everyday objects commonly found in lodging spaces, such as chargers, clocks, remote controls, and everyday objects. The research team evaluated SweepLED on 30 objects that may be found in real-world environments and found that it achieved approximately 94% detection accuracy. It also took less than five seconds to inspect a single object. In addition, the core components of the LED case attached to the smartphone cost less than USD 7, or about KRW 10,000, demonstrating the potential for this technology to be developed into an affordable detection tool that general users can easily access. Professor Jun Han said, “Hidden cameras pose a serious threat to personal safety and privacy in everyday spaces,” adding, “This research is meaningful in that it combines low-cost smartphone-based hardware with AI analysis to present the possibility of a practical detection technology that even non-experts can use.” This paper, with KAIST doctoral student Jonghyuk Yun as first author, was presented on June 20 at ACM MobiSys 2026, one of the leading international conferences in the field of mobile computing. Paper title: Hide-and-Sweep: Detecting Concealed Cameras via LED Illumination Sweeps https://doi.org/10.1145/3812835.3814866 Author information: Jonghyuk Yun (first author), Jaeyoung Moon, Yunseo Park, Sean Rui Xiang Tan, Byunghyun Kim, Rajesh Krishna Balan, and Professor Jun Han (corresponding author) This research was supported by the STEAM Global Convergence Research Support Program and the Mid-Career Researcher Program of the Ministry of Science and ICT and the National Research Foundation of Korea.
2026.08.31 View 5880
KAIST Develops AI to Detect ‘Foreign-Linked Opinion Manipulation’ in 110 Million News Comments During election seasons or major national issues, online news comment sections often become heated spaces of conflict across gender, generation, and political lines. For years, there have been persistent concerns that behind some of these conflicts may lie “foreign winds,” or interventions by foreign actors seeking to manipulate public opinion and deepen social divisions. A KAIST research team has now developed a technology that uses big data on two decades worth of news comments and artificial intelligence (AI) to precisely detect traces of such hidden influence operations. KAIST (President Choongsik Bae) announced on the 12th of August that a joint research team led by Professor Wonjae Lee of the Graduate School of Culture Technology, Professor Meeyoung Cha of the School of Computing (Scientific director at Max Planck Institute for Security and Privacy) and Professor Alice Oh of the School of Computing, in collaboration with Professor Thorsten Holz of the Max Planck Institute, has developed an explainable AI technology that automatically detects patterns suspected of foreign-linked influence operations in online news comments and provides specific evidence for its judgments. The organized and repeated posting of comments or content by certain actors to shape public opinion in a desired direction is known as an “online influence operation.” Existing AI-based detection technologies have had a key limitation: even when they classify certain accounts as belonging to a “blacklist,” they often fail to provide clear evidence explaining why those accounts should be considered influence-operation accounts. To overcome this limitation, the research team used 70 foreign-linked accounts previously identified by the Institute for National Security Strategy as starting points. They then tracked groups of accounts connected to them or repeatedly commenting on the same news articles, ultimately collecting and analyzing a large-scale dataset of 110 million comments posted on Naver News over a 20-year period from 2006 to 2025. In particular, the AI developed by the research team examines accounts through a careful three-step process. First, it checks whether there are clues suggesting that the author may be linked to a foreign source. Second, it examines whether the comment contains emotionally polarizing expressions, such as moral condemnation or blind praise. Third, it identifies which country or target the emotional framing is directed toward. The model does not stop at simply labeling an account as suspicious. It also highlights the specific phrases in the comments that served as the basis for its judgment. The team further combined this with multidimensional behavioral-pattern analysis, including account activity frequency, account lifespan, and activity links with other suspected accounts. As a result, among approximately 4 million Naver News users, the model ultimately identified 23,998 accounts exhibiting patterns consistent with suspected public-opinion manipulation. The analysis also revealed the more subtle strategy of these suspected accounts. Their main target was not the victory of a particular political camp, but rather the maximization of division and confrontation within Korean society. Among the top 10 targets that drew the highest public engagement, measured through likes and other reactions, seven were prominent domestic political figures. Notably, the attacks were not concentrated on a single party or ideology. Former and current presidents, presidential candidates, and political parties from both progressive and conservative camps were targeted across the spectrum. According to the research team, this suggests a sophisticated strategy aimed not so much at supporting a particular group, but at inflaming domestic political conflict and increasing social distrust and polarization. This study is significant because it provides data-based evidence for influence-operation activity that had previously been discussed largely in terms of suspicion, while also offering a potential defense mechanism for protecting healthy online public discourse. In the future, portal platforms and related organizations could use this technology during elections or national crises to monitor the influx of suspicious accounts in real time and prioritize the review of coordinated attacks against domestic political figures. However, the research team emphasized that the AI should not be used to block accounts indiscriminately, but rather as an explainable content-moderation tool that supports the judgment of expert reviewers. Professor Wonjae Lee said, “By analyzing 20 years of data, we found that suspected accounts tended to use messages criticizing Korea and domestic political figures rather than directly praising foreign countries, and that these messages gained higher visibility,” adding, “This research can provide empirical criteria for when and which messages platforms and monitoring organizations should prioritize for review, especially during socially sensitive periods such as elections.” Professor Alice Oh said, “This is a meaningful achievement in which AI precisely identified not only the surface meaning of words in massive comment datasets, but also subtle emotional patterns and organized behavioral signals intended to provoke conflict,” adding, “It can become a powerful defense system against online influence operations, which are becoming increasingly sophisticated.” Professor Meeyoung Cha said, “This study goes beyond simple blacklist-account analysis and represents the outcome of actionable data science that addresses real-world problems and drives practical change,” adding, “In an online environment where social conflict is deepening, we hope this technology will serve as a practical tool for protecting the transparency and trustworthiness of the digital public sphere.” This research was led by KAIST Ph.D. candidate Jaehong Kim and master’s student Hyeonseung Kim as co-first authors. The paper is scheduled to be presented at the USENIX Security Symposium 2026, one of the most prestigious conferences in the field of computer security. Paper title: Cross-National Information Attacks: A Two-Decade Analysis of Troll Behavior in Korea, DOI: 10.48550/arXiv.2606.22785 This research was supported by the Hyundai Motor Chung Mong-Koo Foundation, the Institute of Information & Communications Technology Planning & Evaluation, and the National Research Foundation of Korea, funded by the Ministry of Science and ICT.
2026.08.12 View 2094
"What if there is no one to farm? KAIST reveals a hidden risk to future food security <(From Left) Professor Nicklas Forsell, Professor Hyungjun Kim, Ph.D candidate Hongtak Lee, Professor Haewon Chon> The cause of future food shortages may not be a lack of farmland, but a shortage of agricultural workforce." Amid the reality of low birth rates and rural extinction, a joint international research team from KAIST has developed a new data-driven model that reflects the decline in the agricultural workforce into the analysis of future food security (the ability to stably produce and supply food required by the public). The research findings show that in the future, a shortage of agricultural workforce could act as a key constraint on farmland utilization in most regions of the world. KAIST announced on June 12th that a research team led by Professor Hyungjun Kim from the Department of AI Future (adjunct at the Moon Soul Graduate School of Future Strategy), in joint research with Professor Haewon Chon from the KI Institute for Climate, Environment, and Energy (Graduate School of Green Growth and Sustainability), Professor Nicklas Forsell, and Professor Taikan Oki from the University of Tokyo in Japan, analyzed the impact of the agricultural workforce decline on future food production. < Regional Farmland Supply, Demand, and Shortage Outlook for 2030 and 2100 > Until now, food security and climate change research have mainly focused on "how much farmland can be secured." The approach was to predict the future by calculating whether the climate and soil are suitable for farming and how much food demand will increase in the future. However, the research team asked a different question: "What if there is farmland, but no one to farm it?" In fact, as low birth rates and urban concentration manifest in many countries, the rural population is declining. As economies develop, there is also a stronger tendency for people to move from agriculture to the manufacturing or service sectors. The research team determined that these changes could have a significant impact on future food production. The research team performed the analysis using five future scenarios that combine SSP (Shared Socioeconomic Pathways) and RCP (Representative Concentration Pathways), which are representative international scenario frameworks that predict how future society and climate change will unfold. SSP is a scenario that assumes the direction of societal changes such as population growth, economic growth, and technological development, while RCP is a scenario that shows how the future climate will change depending on greenhouse gas emissions. The research team newly reflected the agricultural workforce variable into these future outlooks. While previous predictions were mainly based on the land available for farming and food demand, this study simultaneously considered the actual number of people who will farm. In other words, the reality that food production can be limited if the agricultural workforce is insufficient, even if farmland and climate conditions are adequate, was reflected in the model. The results of the analysis were even clearer than expected. In the future, it was shown that the farmland area that can actually be utilized will decrease due to the shortage of agricultural workforce in most regions of the world. In some regions, the lack of agricultural workforce was analyzed to act as a greater limiting factor than climate or soil. The research team explained that the agricultural workforce problem may not be easily resolved even in a future where technological development occurs rapidly. Technological development increases the cultivable area per person. However, as industries grow, more people move to the manufacturing and service sectors, which conversely accelerates the decline of the rural population, leading to a reduced workforce and a phenomenon where farmland utilization becomes more restricted. These results suggest the importance of a sustainable development model. In addition, it was confirmed that if international migration is restricted, developed countries will experience a shortage of agricultural workforce, while conversely, the agricultural population in some low-income countries may increase excessively. This shows that migration policies are also closely linked to food security. Professor Hyungjun Kim explained, "This study analyzed future food issues by considering not only climate and land, but also changes in people. It is a study showing that realistic social problems such as low birth rates and the avoidance of rural areas can have a significant impact on future food security and climate change responses." This study, in which Ph.D. student Hongtak Lee from the Moon Soul Graduate School of Future Strategy participated as the first author and Professor Hyungjun Kim from the Department of AI Future conducted as the corresponding author, was published on June 1 in the international academic journal 'Nature Sustainability'. Furthermore, in recognition of its academic importance, the study was prominently highlighted in a separate commentary titled "Farming needs more hands" (News & Views; https://doi.org/10.1038/s41893-026-01841-8) in the same journal. The commentary evaluated this research as "a first step that shifted the conventional question of 'how much land is there' to whether there are enough people and productivity per worker to cultivate that land." ※ Title of Paper: Agricultural Workforce as a Potential Bottleneck of Future Cropland Availability, DOI: https://doi.org/10.1038/s41893-026-01824-9 ※ Main Authors: Hongtak Lee (KAIST, First Author), Nicklas Forsell (KAIST), Taikan Oki (University of Tokyo), Haewon Chon (KAIST), Hyungjun Kim (KAIST, Corresponding Author) <Research Image(AI-generated)> This research was conducted with the support of the AI-based Future Climate Technology Development Framework Program, the Brain Pool Program, and the Plus Project (Ministry of Science and ICT) through the National Research Foundation of Korea.
2026.06.15 View 1722
KAIST Study Provides First Large-Scale Empirical Analysis of Dual-Use Research and Security Oversight <Professor Seokbeom Kwon> A new analysis of approximately 600,000 research papers reveals structural limits to single-country security oversight of dual-use research and identifies trade-offs that policymakers face when strengthening such oversight. KAIST (President Kwang Hyung Lee) announced today that Professor Seokbeom Kwon of the School of Business and Technology Management has published a large-scale empirical analysis examining the structural limitations of tightening security oversight on dual-use research and its potential cost to scientific progress. The study appears in Science on June 5, 2026. Dual-use research (DUR) refers to scientific research that has both legitimate civilian applications—such as vaccine and treatment development—and potential security-sensitive applications, such as biological weapons or bioterrorism. Examples include research on viral transmission mechanisms or pathogen behavior. < Research Image (AI-Generated) > The United States has been strengthening security oversight of dual-use research. Most recently, Executive Order 14292, signed in May 2025, intensified federal oversight of biological research with potential security implications, including dangerous gain-of-function research. The U.S. government also has extended the policy definition of the dual-use research to include broader categories in addition to the gain-of-function research. However, existing policy dialogues have relied primarily on anecdotal evidence and historical case studies. U.S. ex-ante security oversight institutions are based on National Security Decision Directive 189 (NSDD-189) and apply when the federal government is involved in research. Therefore, research conducted without federal government involvement effectively falls outside the jurisdiction of this oversight. Professor Seokbeom Kwon developed a new analytical methodology combining the U.S. Patent and Trademark Office’s multi-stage security review process with patent-paper citation data, and analyzed approximately 600,000 research papers. The work has been recognized in academia for shifting discussions of dual-use research, which had previously relied largely on case-based analysis, toward large-scale empirical analysis. The analysis showed that dual-use research consistently has greater scientific impact than comparable research. This means that research subject to the security oversight tends to play an important role in scientific progress and technological innovation. In addition, the share of dual-use research directly involving the U.S. federal government decreased from about 41% in 1981 to about 22% in 2005, while the share involving foreign institutions increased from 35% to 54% over the same period. This shows that while U.S. security oversight mechanisms based on NSDD-189 have been applied to domestic research, the share of overseas dual-use research has continued to expand. < Comparison of the Scientific Significance of Dual-Use Research > Professor Seokbeom Kwon explained, “Strengthening security oversight on dual-use research by a single country alone may impose disproportionate costs on domestic science, while having structural limits in preventing the development of equally important research conducted overseas,” adding, “To achieve both scientific progress and national security, international cooperation and balanced policy design could contribute to mitigating these structural tensions.” This study provides data-based evidence for international policy discussions surrounding dual-use research. In particular, it is expected to serve as an important reference for future discussions on research security regulation and global cooperation systems not only in biotechnology, but also in advanced technology fields that may be connected to security concerns, such as artificial intelligence (AI) and quantum technology. This study was published as a sole-author paper by Professor Seokbeom Kwon in Science on June 5, 2026. ※ Paper title: “Dual-use research under scrutiny,” DOI: 10.1126/science.aee2479 This research was supported by the National Research Foundation of Korea’s Humanities and Social Sciences Young Researcher Support Program (2025S1A5A8009362).
2026.06.05 View 1490
KAIST Researchers First in the World to Identify Security Threat Exploiting Google Gemini’s "Malicious Expert AI" Structure <Photo 1. (From left) Ph.D. candidates Mingyoo Song and Jaehan Kim, Professor Sooel Son, (Top right) Professor Seungwon Shin, Lead Researcher Seung Ho Na> Most major commercial Large Language Models (LLMs), such as Google’s Gemini, utilize a Mixture-of-Experts (MoE) structure. This architecture enhances efficiency by dynamically selecting and using multiple "small AI models (Expert AIs)" depending on input queries . However, KAIST research team has revealed for the first time in the world that this very structure can actually become a new security threat. A joint research team led by Professor Seungwon Shin (School of Electrical Engineering) and Professor Sooel Son (School of Computing) announced on December 26th that they have identified an attack technique that can seriously compromise the safety of LLMs by exploiting the MoE structure. For this research, they received the Distinguished Paper Award at ACSAC 2025, one of the most prestigious international conferences in the field of information security. ACSAC (Annual Computer Security Applications Conference) is among the most influential international academic conferences in security. This year, only two papers out of all submissions were selected as Distinguished Papers. It is highly unusual for a domestic Korean research team to achieve such a feat in the field of AI security. In this study, the team systematically analyzed the fundamental security vulnerabilities of the MoE structure. In particular, they demonstrated that even if an attacker does not have direct access to the internal structure of a commercial LLM, the entire model can be induced to generate dangerous responses if just one maliciously manipulated "Expert Model" is distributed through open-source channels and integrated into the system. <Figure 1. Conceptual diagram of the attack technology proposed by the research team.> To put it simply: even if there is only one "malicious expert" mixed among normal AI experts, that specific expert may be repeatedly selected for processing harmful queries, causing the overall safety of the AI to collapse. A particularly dangerous factor highlighted was that this process causes almost no degradation in model performance, making the problem extremely difficult to detect in advance. Experimental results showed that the attack technique proposed by the research team could increase the harmful response rate from 0% to up to 80%. They confirmed that the safety of the entire model significantly deteriorates even if only one out of many experts is "infected." This research is highly significant as it presents the first new security threat that can occur in the rapidly expanding global open-source-based LLM development environment. Simultaneously, it suggests that verifying the "source and safety of individual expert models" is now essential—not just performance—during the AI model development process. Professors Seungwon Shin and Sooel Son stated, "Through this study, we have empirically confirmed that the MoE structure, which is spreading rapidly for the sake of efficiency, can become a new security threat. This award is a meaningful achievement that recognizes the importance of AI security on an international level." The study involved Ph.D. candidates Jaehan Kim and Mingyoo Song, Dr. Seung Ho Na (currently at Samsung Electronics), Professor Seungwon Shin, and Professor Sooel Son. The results were presented at ACSAC in Hawaii, USA, on December 12, 2025. <Figure 2. Photo of the Distinguished Paper Award certificate> Paper Title: MoEvil: Poisoning Experts to Compromise the Safety of Mixture-of-Experts LLMs Paper File: https://jaehanwork.github.io/files/moevil.pdf GitHub (Open Source): https://github.com/jaehanwork/MoEvil This research was supported by the Korea Internet & Security Agency (KISA) and the Institute of Information & Communications Technology Planning & Evaluation (IITP) under the Ministry of Science and ICT.
2025.12.26 View 4705
KAIST Researchers Uncover Critical Security Flaws in Global Mobile Networks Breakthrough Discovery Reveals How Attackers Can Remotely Manipulate User Data Without Physical Proximity DAEJEON, South Korea — In an era when recent cyberattacks on major telecommunications providers have highlighted the fragility of mobile security, researchers at the Korea Advanced Institute of Science and Technology have identified a class of previously unknown vulnerabilities that could allow remote attackers to compromise cellular networks serving billions of users worldwide. The research team, led by Professor Yongdae Kim of KAIST's School of Electrical Engineering, discovered that unauthorized attackers could remotely manipulate internal user information in LTE core networks — the central infrastructure that manages authentication, internet connectivity, and data transmission for mobile devices and IoT equipment. The findings, presented at the 32nd ACM Conference on Computer and Communications Security in Taipei, Taiwan, earned the team a Distinguished Paper Award, one of only 30 such honors selected from approximately 2,400 submissions to one of the field's most prestigious venues. A New Class of Vulnerability The vulnerability class, which the researchers termed "Context Integrity Violation" (CIV), represents a fundamental breach of a basic security principle: unauthenticated messages should not alter internal system states. While previous security research has primarily focused on "downlink" attacks — where networks compromise devices — this study examined the less-scrutinized "uplink" security, where devices can attack core networks. "The problem stems from gaps in the 3GPP standards," Professor Kim explained, referring to the international body that establishes operational rules for mobile networks. "While the standards prohibit processing messages that fail authentication, they lack clear guidance on handling messages that bypass authentication procedures entirely." The team developed CITesting, the world's first systematic tool for detecting these vulnerabilities, capable of examining between 2,802 and 4,626 test cases — a vast expansion from the 31 cases covered by the only previous comparable research tool, LTEFuzz. Widespread Impact Confirmed Testing four major LTE core network implementations — both open-source and commercial systems — revealed that all contained CIV vulnerabilities. The results showed: Open5GS: 2,354 detections, 29 unique vulnerabilities srsRAN: 2,604 detections, 22 unique vulnerabilities Amarisoft: 672 detections, 16 unique vulnerabilities Nokia: 2,523 detections, 59 unique vulnerabilities The research team demonstrated three critical attack scenarios: denial of service by corrupting network information to block reconnection; IMSI exposure by forcing devices to retransmit user identification numbers in plaintext; and location tracking by capturing signals during reconnection attempts. Unlike traditional attacks requiring fake base stations or signal interference near victims, these attacks work remotely through legitimate base stations, affecting anyone within the same MME (Mobility Management Entity) coverage area as the attacker — potentially spanning entire metropolitan regions. Industry Response and Future Implications Following responsible disclosure protocols, the research team notified affected vendors. Amarisoft deployed patches, and Open5GS integrated the team's fixes into its official repository. Nokia, however, stated it would not issue patches, asserting compliance with 3GPP standards and declining to comment on whether telecommunications companies currently use the affected equipment. "Uplink security has been relatively neglected due to testing difficulties, implementation diversity, and regulatory constraints," Professor Kim noted. "Context integrity violations can pose serious security risks." The research team, which included KAIST doctoral students Mincheol Son and Kwangmin Kim as co-first authors, along with Beomseok Oh and Professor CheolJun Park of Kyung Hee University, plans to extend their validation to 5G and private 5G environments. The tools could prove particularly critical for industrial and infrastructure networks, where breaches could have consequences ranging from communication disruption to exposure of sensitive military or corporate data. The research was supported by the Ministry of Science and ICT through the Institute for Information & Communications Technology Planning & Evaluation, as part of a project developing security technologies for 5G private networks. With mobile networks forming the backbone of modern digital infrastructure, the discovery underscores the ongoing challenge of securing systems designed in an era when such sophisticated attacks were barely conceivable — and the urgent need for updated standards to address them.
2025.11.03 View 4841
The Fall of Tor for Just $2: A Solution to the Tor Vulnerability <(From Left) Ph.D candidate Jinseo Lee, Hobin Kim, Professor Min Suk Kang> KAIST research team has made a new milestone in global security research, becoming the first Korean research team to identify a security vulnerability in Tor, the world's largest anonymous network, and propose a solution. On September 12, our university's Professor Min Suk Kang's research team from the School of Computing announced that they had received an Honorable Mention Award at the USENIX Security 2025 conference, held from August 13 to 15 in Seattle, USA. The USENIX Security conference is one of the world's most prestigious conferences in information security, ranking first among all security and cryptography conferences and journals based on the Google Scholar h-5 index. The Honorable Mention Award is a highly regarded honor given to only about 6% of all papers. The core of this research was the discovery of a new denial-of-service (DoS) attack vulnerability in Tor, the world's largest anonymous network, and the proposal of a method to resolve it. The Tor Onion Service, a key technology for various anonymity-based services, is a primary tool for privacy protection, used by millions of people worldwide every day. The research team found that Tor's congestion-sensing mechanism is insecure and proved through a real-world network experiment that a website could be crippled for as little as $2. This is just 0.2% of the cost of existing attacks. The study is particularly notable as it was the first to show that the existing security measures implemented in Tor to prevent DoS attacks can actually make the attacks worse. In addition, the team used mathematical modeling to uncover the principles behind this vulnerability and provided guidelines for Tor to maintain a balance between anonymity and availability. These guidelines have been shared with the Tor development team and are currently being applied through a phased patch. A new attack model proposed by the research team shows that when an attacker sends a tiny, pre-designed amount of attack traffic to a Tor website, it confuses the congestion measurement system. This triggers an excessive congestion control, which ultimately prevents regular users from accessing the website. The research team proved through experiments that the cost of this attack is only 0.2% of existing methods. In February, Tor founder Roger Dingledine visited KAIST and discussed collaboration with the research team. In June, the Tor administration paid a bug bounty of approximately $800 in appreciation for the team's proactive report. "Tor anonymity system security is an area of active global research, but this is the first study on security vulnerabilities in Korea, which makes it very significant," said Professor Kang Min-seok. "The vulnerability we identified is very high-risk, so it received significant attention from many Tor security researchers at the conference. We will continue our comprehensive research, not only on enhancing the Tor system's anonymity but also on using Tor technology in the field of criminal investigation." The research was conducted by Ph.D. candidate Jinseo Lee (first author), and former master's student Hobin Kim at the KAIST Graduate School of Information Security and a current Ph.D. candidate at Carnegie Mellon University (second author). The paper is titled "Onions Got Puzzled: On the Challenges of Mitigating Denial-of-Service Problems in Tor Onion Services." https://www.usenix.org/conference/usenixsecurity25/presentation/lee This achievement was recognized as a groundbreaking, first-of-its-kind study on Tor security vulnerabilities in Korea and played a decisive role in the selection of Professor Kang's lab for the 2025 Basic Research Program (Global Basic Research Lab) by the Ministry of Science and ICT. < Photo 2. Presentation photo of Ph.D cadidate Jinseo Lee from School of Computing> Through this program, the research team plans to establish a domestic research collaboration system with Ewha Womans University and Sungshin Women's University and expand international research collaborations with researchers in the U.S. and U.K. to conduct in-depth research on Tor vulnerabilities and anonymity over the next three years. < Photo 3. Presentation photo of Ph.D cadidate Jinseo Lee from School of Computing>
2025.09.12 View 3917
KAIST–National Intelligence Service Successfully Hold the 4th University Research Security Education Council Workshop KAIST (President Kwang Hyung Lee) announced on the 22nd of August that, together with the National Intelligence Service, it held the “4th University Research Security Education Council Workshop” at the Academic Cultural Complex on KAIST’s main campus in Daejeon on the 21st. This 4th workshop was held under the theme of “Global Research Security,” based on the recognition that technology leakage can have serious impacts not only on the national economy and security but on international exchanges in science and technology. Accordingly, national policies and outstanding cases from institutions to enhance the level of university research security management were shared, and practical security enhancement measures applicable in the field were discussed. In the presentation sessions, △ the Ministry of Science and ICT presented the government’s policy direction for strengthening research security, △ the National Institute for International Education and Training (KIRD) gave a presentation on settlement and career support programs for foreign researchers in science and engineering, △ the Korea Strategic Trade Institute (KOSTI) introduced the Authorized Economic Operator (AEO/CP) system for universities’ dual-use items and technology export control measures, and △ the Korea Institute of S&T Evaluation and Planning (KISTEP) conducted a special lecture on research security field manuals. In the university case presentations, KAIST shared achievements in promoting global research security, while Yong In University introduced its newly launched program this year for fostering security professionals. This event was attended by more than 130 participants from approximately 70 universities nationwide, including Seoul National University and Ewha Womans University, as well as officials from the Ministry of Science and ICT, the National Intelligence Service, KIRD, KISTEP, and KOSTI. An NIS official stated, “With the rapid changes in the university research environment due to the expansion of international joint research and the increase in international students, the establishment of a research security culture has become more important than ever. Based on the excellent cases shared at the workshop, we will actively reflect the opinions of host institutions to spread security awareness and continue preparing effective countermeasures.” Cheol Seong Jang, President of the National University Council of Research & Industry Cooperation and Research Directors (Professor at Kangwon National University), emphasized, “If the ideas and cases discussed at the workshop are applied in university settings, they will greatly contribute to Korea leading the establishment of a trusted global research ecosystem.” Byung-Kwan Cho, Director of Research at KAIST, said, “KAIST will not hesitate to introduce and spread leading research security systems so that our efforts to strengthen research security become a benchmark for other universities. By applying the principle of open science — ‘as open as possible, as closed as necessary’ — we will ensure that research security and global exchanges achieve harmony.” The University Research Security Education Council was launched in June 2022 with joint supervision by KAIST and the National Intelligence Service and cooperation from KIRD. This year’s workshop was co-hosted by KAIST, the National Intelligence Service, and Kangwon National University.
2025.08.22 View 3644
The World’s First Hacking-preventing Cryptographic Semiconductor Chip With the dramatic increase in the amount of information exchanged between components or devices in the 5G/6G era, such as for the Internet of Things (IoT) and autonomous driving, hacking attacks are becoming more sophisticated. Consequently, enhancing security functions is essential for safely transmitting data between and among devices. On February 29th, a KAIST research team led by Professors Yang-gyu Choi and Seung-tak Ryu from the School of Electrical Engineering announced the successful development of the world's first security cryptographic semiconductor. The team has developed the Cryptoristor, a cryptographic transistor based on FinFET technology, produced through a 100% silicon-compatible process, for the first time in the world. Cryptoristor is a random number generator (RNG) with unparalleled characteristics, featuring a unique structure comprising a single transistor and a distinctive mechanism. In all security environments, including artificial intelligence, the most crucial element is the RNG. In the most commonly used security chip, the Advanced Encryption Standard (AES), the RNG is a core component, occupying approximately 75% of the total chip area and more than 85% of its energy consumption. Hence, there is an urgent need for the development of low-power/ultra-small RNGs suitable for mobile or IoT devices. Existing RNGs come with limitations as they lack compatibility with silicon CMOS processes and circuit-based RNGs occupy a large surface area. In contrast, the team’s newly developed Cryptoristor, a cryptographic semiconductor based on a single-component structure, consumes and occupies less than .001 of the power and area compared to the current chips being used. Utilizing the inherent randomness of FinFETs, fabricated on a Silicon-on-Insulator (SOI) substrate with an insulating layer formed beneath the silicon, the team developed an RNG that unpredictably produces zeroes and ones. < Figure 1. Conceptual diagram of the security cryptographic transistor device. > Generally speaking, preventing hackers from predicting the encrypted algorithms during data exchanges through mobile devices is pivotal. Therefore, this method ensures unpredictability by generating random sequences of zeroes and ones that change every time. Moreover, while the Cryptoristor-based RNG research is the world's first of its kind without any international implementation cases, it shares the same transistor structure as existing logic or memory components. This enables 100% production through rapid mass production processes using existing semiconductor facilities at a low cost. Seung-il Kim, a PhD student who led the research, explained the significance of the study, stating, "As a cryptographic semiconductor, the ultra-small/low-power random number generator enhances security through its distinctive unpredictability, supporting safe hyperconnectivity with secure transmissions between chips or devices. Particularly, compared to previous research, it offers excellent advantages in terms of energy consumption, integration density, and cost, making it suitable for IoT device environments." This research, with master’s student Hyung-jin Yoo as the co-author, was officially published in the online edition of Science Advances, a sister journal of Science, in February 2024 (research paper title: Cryptographic transistor for true random number generator with low power consumption). This research received support from the Next-Generation Intelligent Semiconductor Technology Development Project and the Core Technology Development Project for the National Semiconductor Research Laboratory.
2024.03.07 View 17030
Professor Sang Kil Cha Receives IEEE Test-of-Time Award Professor Sang Kil Cha from the Graduate School of Information Security (GSIS) in the School of Computing received the Test-of-Time Award from IEEE Security & Privacy, a top conference in the field of information security. The Test-of-Time Award recognizes the research papers that have influenced the field of information security the most over the past decade. Three papers were selected this year, and Professor Cha is the first Korean winner of the award. The paper by Professor Cha was published in 2012 under the title, “Unleashing Mayhem on Binary Code”. It was the first to ever suggest an algorithm that automatically finds bugs in binary code and creates exploits that links them to an attack code. The developed algorithm is a core technique used for world-class cyber security hacking competitions like the Cyber Grand Challenge, an AI hacking contest. Starting with this research, Professor Cha has carried out various studies to develop technologies that can find bugs and vulnerabilities through binary analyses, and is currently developing B2R2, a Korean platform that can analyze various binary codes.
2022.06.13 View 10816
Team Geumo Wins Consecutive Victories in K-Cyber Security Challenge < Professor Sang Kil Cha > < Masters Candidate Kangsu Kim and Researcher Corentin Soulet > Team Geumo, led by Professor Sang Kil Cha from the Graduate School of Information Security, won the K-Cyber Security Challenge in the AI-based automatic vulnerability detection division for two consecutive years in 2018 and 2019. The K-Cyber Security Challenge is an inter-machine hacking competition. Participants develop and operate AI-based systems that are capable of independently identifying software vulnerabilities and gaining operating rights through hacking. The K-Cyber Security Challenge, inspired by the US Cyber Grand Challenge launched by the Defense Advanced Research Projects Agency (DARPA), is hosted by the Ministry of Science and ICT and organized by the Korea Internet and Security Agency. Researcher Corentin Soulet of the School of Computing and master’s student Kangsu Kim of the Graduate School of Information Security teamed up for the competition. Professor Cha, who has led the research on software and systems security since his days at Carnegie Mellon University, succeeded in establishing a world-class system using domestic technology. In a recent collaboration with the Cyber Security Research Center, Professor Cha achieved a ten-fold increase in the speed of binary analysis engines, a key component of AI-based hacking systems. For this accomplishment, he received the Best Paper Award at the 2019 Network and Distributed System Security Workshop on Binary Analysis Research (NDSS BAR). Kangsu Kim said, "It is a great honor to win the competition two years in a row. I will continue to work hard and apply my knowledge to serve society.” (END)
2019.12.20 View 18270
Anti-drone Technology for Anti-Terrorism Applications (from top right clockwise: Professor Yongdae Kim, PhD Candidates Yujin Kwon, Juhwan Noh, Hocheol Shin, and Dohyun Kim) KAIST researchers have developed anti-drone technology that can hijack other drones by spoofing its location using fake GPS signals. This technology can safely guide a drone to a desired location without any sudden change in direction in emergency situations, and thus respond effectively to dangerous drones such as those intending to carry out acts of terrorism. Advancements in the drone industry have led to the wider use of drones in our daily lives in areas of reconnaissance, searching and rescuing, disaster prevention and response, and delivery services. At the same time, there has also been a growing concern about privacy, safety, and security issues regarding drones, especially those arising from intrusion into private property and secure facilities. Therefore, the anti-drone industry is rapidly expanding to detect and respond to this possible drone invasion. The current anti-drone systems used in airports and other key locations utilize electronic jamming signals, high-power lasers, or nets to neutralize drones. For example, drones trespassing on airports are often countered with simple jamming signals that can prevent the drones from moving and changing position, but this may result in a prolonged delay in flight departures and arrivals at the airports. Drones used for terrorist attacks – armed with explosives or weapons – must also be neutralized a safe distance from the public and vital infrastructure to minimize any damage. Due to this need for a new anti-drone technology to counter these threats, a KAIST research team led by Professor Yongdae Kim from the School of Electrical Engineering has developed technology that securely thwarts drones by tricking them with fake GPS signals. Fake GPS signals have been used in previous studies to cause confusion inside the drone regarding its location, making the drone drift from its position or path. However, such attack tactics cannot be applied in GPS safety mode. GPS safety mode is an emergency mode that ensures drone safety when the signal is cut or location accuracy is low due to a fake GPS signals. This mode differs between models and manufacturers. Professor Kim’s team analyzed the GPS safety mode of different drone models made from major drone manufacturers such as DJI and Parrot, made classification systems, and designed a drone abduction technique that covers almost all the types of drone GPS safety modes, and is universally applicable to any drone that uses GPS regardless of model or manufacturer. The research team applied their new technique to four different drones and have proven that the drones can be safely hijacked and guided to the direction of intentional abduction within a small margin of error. Professor Kim said, “Conventional consumer drones equipped with GPS safety mode seem to be safe from fake GPS signals, however, most of these drones are able to be detoured since they detect GPS errors in a rudimentary manner.” He continued, “This technology can contribute particularly to reducing damage to airports and the airline industry caused by illegal drone flights.” The research team is planning to commercialize the developed technology by applying it to existing anti-drone solutions through technology transfer.” This research, featured in the ACM Transactions on Privacy and Security (TOPS) on April 9, was supported by the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD). Image 1. Experimental environment in which a fake GPS signal was produced from a PC and injected into the drone signal using directional antennae Publication: Juhwan Noh, Yujin Kwon, Yunmok Son, Hocheol Shin, Dohyun Kim, Jaeyeong Choi, and Yongdae Kim. 2019. Tractor Beam: Safe-hijacking of Consumer Drones with Adaptive GPS Spoofing. ACM Transactions on Privacy and Security. New York, NY, USA, Vol. 22, No. 2, Article 12, 26 pages. https://doi.org/10.1145/3309735 Profile: Prof. Yongdae Kim, MS, PhD yongdaek@kaist.ac.kr https://www.syssec.kr/ Professor School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon 34141, Korea Profile: Juhwan Noh, PhD Candidate juhwan@kaist.ac.kr PhD Candidate System Security (SysSec) Lab School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon 34141, Korea (END)
2019.06.25 View 51849