Moguće opasnosti generativne veštačke inteligencije

Generativna veštačka inteligencija (GenAI) predstavlja oblast proučavanja koja obuhvata razvoj velikih modela sa milijardama parametara, omogućavajući generisanje sadržaja u različitim medijima. Zahvaljujući svojoj sposobnosti da unapredi efikasnost i ekonomsku konkurentnost GenAI je već našla široku primenu u brojnim oblastima, kao što su bankarstvo, zdravstvo, biologija, saobraćaj, visoko obrazovanje, energetika i druge. Međutim, pored brojnih prednosti, GenAI istovremeno nosi i značajne bezbednosne rizike. Primeri zloupotrebe sistema GenAIobuhvataju kreiranje dezinformacija, organizovanje ciljanih fišing kampanjai druge manipulativne aktivnosti. Pored toga, sofisticirani napadi poput suparničkih, ubacivanja malicioznih unosa (prompt injection), ekstrakcije podataka i inverzije modela ciljaju ranjivosti GenAI sistema kako bi manipulisali korisnicima ili ostvarili finansijsku korist. Upravo zbog ovih izazova, neophodno je temeljno analizirati potencijalne rizike u stvarnim scenarijima i istražiti odgovarajuće strategije ublažavanja, čime bi se obezbedila bezbedna i etička primena GenAI u oblastima od ključno

AI incidents and data integrity

Artificial intelligence (AI) has experienced widespread adoption across diverse sectors due to its capacity to enhance operational efficiency and economic competitiveness. However, the deployment of AI systems has also simultaneously introduced numerous security challenges and potential risks which demand careful consideration. As investments in AI development have increased substantially, corresponding investments in cybersecurity have become more critical. Ensuring the secure implementation of AI, particularly within critical infrastructure systems, necessitates the development of robust and resilient systems. Analysing real world AI incidents provides valuable insights which may serve to enhance security mechanisms and prevent future vulnerabilities. Since the inception of artificial intelligence, researchers have identified various system vulnerabilities and associated risks. Promoting awareness of such potential AI hazards has proven instrumental in facilitating a deeper understanding of both the scope and severity of these risks. Moreover, such awareness provides a framework for developing AI tools which are not only more resilient, but also ethically sound. It is essential to examine how these dynamics evolve in practical environments by systematically identifying such incidents, their underlying causes, and their consequent impacts, rather than relying solely on theoretical projections. In response to this urgent need, AI incident databases have emerged as crucial instruments for responsible AI development and governance. The primary objective of these initiatives is to methodically document and categorise incidents, thereby strengthening security measures, informing preventative strategies, and fostering transparency and accountability within AI systems management.https://www.mi.sanu.ac.rs/~ai_conf

Između polova: Uticaj polarnosti na opseg strateških opcija malih država

Polazeći od Tukididovog zapažanja da snažni čine ono što mogu, a slabi trpe ono što moraju, u radu se razmatraju načini na koje različiti oblici polarnosti međunarodne strukture utiču na opseg strateških opcija malih država. Tragajući za pružanjem generalizovanog objašnjenja uticaja polarnosti na strateške opcije malih država, centralna teza rada jeste da različiti oblici polarnosti omogućavaju različit stepen slobode delanja država. Sa povećanjem broja polova, povećava se i sloboda delanja i opseg strateških opcija malih država usled dva ključna razloga. Prvi je što prisustvo više moćnih aktera ograničava jednostrano delovanje, pošto velike sile moraju da uzimaju u obzir reakcije drugih sila. Ovo proizvodi opreznije ponašanje i manju sklonost ka agresiji prema slabijima. Drugi je što veći broj velikih sila otvara nove prilike malim državama za delanje zbog mogućnosti implementacije strategija koje počivaju na trijadnim interakcijama. U radu se zaključuje da iako su materijalne sposobnosti važne, u multipolarnim sistemima umešno državništvo i dobra strategija mogu znatno povećati mogućnost malih država da ostvare šire interese od pukog preživljavanja. Na kraju, u radu se iskazuje i oprez da iako multipolarni sistemi omogućavaju veću fleksibilnost i prostor za aktivnu spoljnu politiku, oni nose veću opasnost po stabilnost i opstanak, što je naročit slučaj u trenutnom momentu kada je sistem u strukturalnom fluksu

Disinformation and generative AI: risks, chalenges and possible solutions

The disinformation phenomenon has been present since the very dawn of human communication, with repeated exposure to false information often resulting in its acceptance as truth. In contemporary society, however, disinformation has become increasingly alarming due to the simple, inexpensive, and convincing way it can be created using generative artificial intelligence and large language models in particular. These models, which continue to make significant advancements, enable the generation of large volumes of content which appear credible, even in the national language. In addition, such models facilitate the creation of personalised content tailored to certain groups or individuals, citing seemingly credible sources, and thus serving to further strengthen the impact of disinformation. In a world inundated with information, this problem has become even more pronounced. One of the key channels for the spread of disinformation is social media, whose primary goal is to capture and maintain their users' attention with content which reinforces their existing beliefs. This paper will present the potential uses of generative AI in the creation of disinformation, as well as possible solutions to address this problem.Fenomen dezinformacija prisutan je od samih početaka ljudske komunikacije, pri čemu ponovljena izloženost lažnim informacijama često dovodi do njihovog prihvatanja kao istine. U savremenom društvu, međutim, dezinformacije postaju sve zabrinjavajuće zbog jednostavnog, jeftinog i uverljivog načina na koji se mogu kreirati koristeći generativnu veštačku inteligenciju, naročito velike jezičke modele. Generartna veštčka inteligencija, koji i dalje prave značajne napretke, omogućavaju generisanje velikih količina sadržaja koji izgledaju verodostojno, čak i na nacionalnom jeziku. Pored toga, ovi modeli olakšavaju kreiranje personalizovanog sadržaja prilagođenog određenim grupama ili pojedincima, navodeći prividno pouzdane izvore, čime dodatno jačaju uticaj dezinformacija. U svetu preplavljenom informacijama, ovaj problem postaje još izraženiji. Jedan od ključnih kanala za širenje dezinformacija su socijalni mediji, čiji je primarni cilj da privuku i zadrže pažnju korisnika sadržajem koji potvrđuje njihova postojeća uverenja. U radu je predstvljen potencijal upotrebe generativne veštačke inteligencije u kreiranju dezinformacija, kao i mogućnosti za suzbijanje ovog problema.preprin

Clausewitz's conceptualization of war as an ontological starting point in its research and understanding

Рат је, као изразито деструктивна и комплексна друштвена појава, одувек био предмет научног интересовања и сагледавања у друштвеним и хуманистичким наукама, с обзиром на последице које оставља на људско друштво у целини. Циљ рада је да се, на основу анализе досадашњих научних истраживања о рату, сагледају основни проблеми у изучавању ове друштвене појаве и понуде одређени аргументи зашто би Клаузевицева концептуализација рата било добро онтолошко полазиште за свеобухватнија и целовитија научна истраживања. Иако има несумњиво значајну друштвену улогу, рат се, у друштвеним и хуманистичким наукама само парцијално изучава. Конкретно, разматрају се поједини аспекти рата, узроци настанка и његове последице у различитим областима друштвеног живота, анализирају се појединачни ратови и оружани сукоби, док је врло мало дела посвећено сагледавању његове природе и суштинских каракте- ристика. Разлози за овако „запостављање” рата су бројни и комплексни. Један од глав- них разлога је његова стигматизација, као аморалне и деструктивне друштвене појаве, при чему се његова улога у развоју људског друштва, често игнорише и осуђује. Због опште прихваћене друштвене перцепције о штетности рата, ова друштвена појава се истражује парцијално, најчешће из перспективе интересовања појединих научних дисциплина, односно сагледава се његова повезаност и релације са појавама које су непосредан предмет истраживања конретне науке, или научне области. Због друштвене стигматизације рата и парцијалног изучавања ове појаве, рат је перципиран као исувише комплексна друштвена појава коју није могуће сагледати кроз призму једне науке, или научне области, па се прибегло мулти- дисциплинарном изучавању рата. Међутим, слабости парцијалног истраживања рата нису превазиђена. Клаузевицева концептуализација рата, као скуп замисли и идеја које одража- вају суштинске одреднице ове појаве, представља добру онтолошку основу за даља истраживања. Управо разумевање природе и суштине рата, његових за- конитости и принципа, односно начина на који „парадоксално тројство”, „клима” рата, „магла” рата и „фрикција” утичу на ток и исход оружаног сукоба предста- вљају немерљив допринос војној теорији и пракси. Теоријску релеватност и трајност Клаузевицевих идеја и ставова о рату потврђује и чињеница да се његове замисли о природи ове друштвене појаве изучавају у свим војним организација- ма и институцијама широм света. Клаузевицеве теоријске поставке и замисли о рату, иако написане пре готово 150 година, издржале су бројне критике, нарочито савремених теоретичара рата, што указује на ванвременску вредност и валидност Клаузевицевих идеја, без обзира на одређене мањкависти у погледу теоријске некохрентности и магловитости, које су вероватно настале због немогућности самог Клаузевица да доврши своје капитално дело.As a one-of-a-kind and complex social phenomenon, war has always been a subject of interest and analysis of different fields of science, chiefly the social and humanistic sciences. Because of its complexity, disciplinary limitations of the fields of science that researched it, and social stigmatisation, war was only partially studied in social and humanistic sciences. Such approach did not facilitate complete understanding of the nature of war, or gaining insight into its core characteristics, relations of cause and effect and links, as well as key processes that take place within it. On the other hand, because of a multidisciplinary consideration of the essence of war in research done so far, that were dominated by the perception that war was too complex and unpredictable phenomenon to be studied only by one field of study, military theory remained underdeveloped, supressed by scientifically and theoretically constructed sciences. To overcome this problem, Clausewitz‘s conceptualisation of war represents a suitable ontological starting point for an all-encompassing scientific insight and understanding of war. Although conceived more than a century and a half ago, Clausewitz‘s theoretical postulates of war withstood the criticism of numerous theoreticians who have not managed to confute their validity and durability. Clausewitz‘s ideas about the nature and character of war, the processes taking place within this phenomenon and relations between warring parties, and the role of war as a political instrument, represent a good theoretical base for further research. Characters of contemporary wars, however more complex and different from the wars from previous epochs, still confirm the timeless value of Clausewitz‘s ideas

Fake News and Generative Artificial Intelligence: Risks and Potential Solutions

The fake news phenomenon has been present since the very dawn of human communication, with repeated exposure to false information often resulting in its acceptance as truth. In contemporary society, however, fake news has become extremely dangerous due to the simple, inexpensive, and convincing way it can be created using generative artificial intelligence, and large language models in particular. These models, which continue to make significant advancements, enable the generation of large volumes of content which appear credible, even in the Serbian language. In addition, such models facilitate the creation of personalised content tailored to certain groups or individuals, citing seemingly credible sources, and thus serving to further strengthen the impact of fake news. In a world inundated with information, this problem has become even more pronounced. One of the key channels for the spread of fake news is social media, whose primary goal is to capture and maintain their users' attention with content which reinforces their existing beliefs. This process results in an echo chamber effect, where users receive information which in turn reinforces their preconceptions, making it harder to recognise false information. A secondary, but equally concerning issue caused by fake news is the creation of distrust and confusion among users, ultimately leading to scepticism towards even accurate information. The vast amount of data on social media highlights the need for the application of artificial intelligence, not only to deliver relevant content, but also to detect fake news. Although AI-based approaches can be used to identify false information, questions remain as to the transparency and reliability of these algorithms in carrying out this task. This paper will present the potential uses of artificial intelligence in the creation of fake news, as well as possible solutions to address this problem.https://judig.jerteh.rs

Disaster Risk Reduction Education Through Digital Technologies in the Context of Education for Sustainable Development: A Curricula Analysis of Security and Defense Studies in Serbia

This study examines the integration of disaster risk reduction (DRR) into security and defense studies curricula at Serbian universities, focusing on public and private institutions. As climate change accelerates and natural disasters become more frequent, addressing these risks is critical for national security and sustainable development. This research evaluates the extent of DRR incorporation in curricula and the use of emerging technologies in DRR education. A qualitative analysis of programs at institutions such as the Faculty of Security Studies at the University of Belgrade, the Military Academy, the University of Criminal Investigation and Police Studies, and private universities like Singidunum and Educons University reveals that public institutions have made significant progress. However, private universities still need comprehensive DRR-focused courses and technological integration. This study recommends fostering collaboration between public and private universities, expanding access to the National Simulation Center, and incorporating modern technologies and active learning strategies across curricula to bridge existing gaps. These steps equip future security professionals with the practical skills and interdisciplinary knowledge necessary for effective disaster management in an increasingly complex risk environment

ARTIFICIAL INTELLIGENCE AND CYBER SECURITY: POSSIBILITIES AND RISKS

Artificial Intelligence (AI), especially Machine Learning (ML), has become widespread. At the same time, cyber security attacks are increasing in number, sophistication, severity, and financial impact. Mitigating these attacks is crucial in sensitive environments, such as critical infrastructure, particularly in sectors like the nuclear industry, where the consequences of such attacks can be devastating. Machine Learning has proven effective in analyzing large datasets and identifying patterns that were previously unknown or not obvious but useful. In this way, ML can be utilized to detect cyber attacks and block attackers. However, it can also be exploited in attacks on targeted systems, analyzing system infiltration and uncovering software vulnerabilities. Additionally, in adversarial attacks on machine learning models, the weaknesses of ML systems and their reliance on data are explored. Adversarial attacks on ML models can involve malicious manipulation of input data to deceive the models and cause incorrect decisions, potentially leading to unsafe operational outcomes in the nuclear facilities. For example, attackers could introduce small perturbations to operational data, causing the ML algorithm to incorrectly assess the system's stability. Ultimately, these attacks could compromise the safety, security, and reliability of the system, with potentially devastating consequences if not properly mitigated. Therefore, machine learning algorithms intended to secure a system must be resilient themselves. Research on adversarial attacks and defense mechanisms for ML algorithms used in the nuclear facilities is limited, highlighting the need for continued study in this area.Conference Book of Abstract