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G. Smaragdakis
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1
The Last of the Apaches
Investigating the State of Internet-facing End-of-Life Software
Conference paper
(2026)
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Ioannis Arakas, Panagiotis Pallis, Evangelos Markatos, Georgios Smaragdakis
In the software development life-cycle, new software packages are deployed while older ones are phased out as they reach their “End of Life” and are no longer supported. Despite this lack of support, some of these End-of-Life (EoL) software distributions are still popular and are being used. However, running EoL software poses massive security risks as older software may contain vulnerabilities for which security updates are no longer available. In this paper we investigate the prevalence of EoL software in Internet-facing devices. To our surprise, we find that more than 6 million out of the 44.3 million hosts we consider in our study are running at least one EoL version of very popular software, including web server software, software libraries, databases, and scripting languages. In addition, NIST identifies some of these EoL versions as highly vulnerable and highly or critically severe (severity score higher than 7 and 9 respectively). To identify which networks are at greater risk, we investigate regions and networks with a high concentration of hosts running EoL software. Our work aims to raise awareness within both the research and operational communities about the current state of End-of-Life (EoL) software and the potential risks associated with its continued large-scale use.
...
In the software development life-cycle, new software packages are deployed while older ones are phased out as they reach their “End of Life” and are no longer supported. Despite this lack of support, some of these End-of-Life (EoL) software distributions are still popular and are being used. However, running EoL software poses massive security risks as older software may contain vulnerabilities for which security updates are no longer available. In this paper we investigate the prevalence of EoL software in Internet-facing devices. To our surprise, we find that more than 6 million out of the 44.3 million hosts we consider in our study are running at least one EoL version of very popular software, including web server software, software libraries, databases, and scripting languages. In addition, NIST identifies some of these EoL versions as highly vulnerable and highly or critically severe (severity score higher than 7 and 9 respectively). To identify which networks are at greater risk, we investigate regions and networks with a high concentration of hosts running EoL software. Our work aims to raise awareness within both the research and operational communities about the current state of End-of-Life (EoL) software and the potential risks associated with its continued large-scale use.
In this paper, we present methodologies based on two Internet Control Message Protocol (ICMP) message types to measure subnet deployments on the Internet. First, we exploit a peculiar behavior in certain router implementations, together with variations in the interpretation of protocol specifications, to infer prefix boundaries and thus prefix lengths through remotely probing the Internet with specially crafted ICMP Echo requests. Second, we evaluate the extent to which hosts continue to respond to the deprecated ICMP Address Mask request. We assess the applicability of these methods across devices from different network vendors and discuss protocol quirks that arise from lenient interpretations of the specifications. By combining both methods, we evaluate the consistency of our approach and present a dataset comprising 3.8M subnets. This dataset covers 15% of BGP announced prefixes across more than 20k autonomous systems. Using this dataset, we examine the uniformity of host responsiveness within autonomous systems and across subnet sizes, finding that smaller subnets tend to exhibit greater uniformity. Ultimately, our work demonstrates the feasibility of subnet inference, at a reasonable Internet scale.
...
In this paper, we present methodologies based on two Internet Control Message Protocol (ICMP) message types to measure subnet deployments on the Internet. First, we exploit a peculiar behavior in certain router implementations, together with variations in the interpretation of protocol specifications, to infer prefix boundaries and thus prefix lengths through remotely probing the Internet with specially crafted ICMP Echo requests. Second, we evaluate the extent to which hosts continue to respond to the deprecated ICMP Address Mask request. We assess the applicability of these methods across devices from different network vendors and discuss protocol quirks that arise from lenient interpretations of the specifications. By combining both methods, we evaluate the consistency of our approach and present a dataset comprising 3.8M subnets. This dataset covers 15% of BGP announced prefixes across more than 20k autonomous systems. Using this dataset, we examine the uniformity of host responsiveness within autonomous systems and across subnet sizes, finding that smaller subnets tend to exhibit greater uniformity. Ultimately, our work demonstrates the feasibility of subnet inference, at a reasonable Internet scale.
Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary relationships among malware families can inform proactive defense, but traditional reverse engineering can take months to years to uncover such lineage relationships. We propose MalTree, a framework that applies bioinformatics-inspired phylogenetic techniques (UPGMA and Neighbor-Joining) at scale to model malware evolution automatically using structural, behavioral, and image-based features. We introduce temporal validation using VirusTotal timestamps to assess whether inferred trees reflect actual evolutionary order. MalTree achieves 87% temporal consistency, indicating that inferred evolutionary relationships closely align with real-world emergence timelines. Our analysis shows that some families mutate over 10 times faster than others, suggesting that detection strategies should be tailored to family-specific evolutionary tempos. Case studies, including the Mirai botnet, confirm that inferred relationships from our phylogenetic tree align with documented threat intelligence. Our framework provides a foundation for shifting malware analysis from sample-by-sample classification toward lineage-aware evolutionary modeling.
...
Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary relationships among malware families can inform proactive defense, but traditional reverse engineering can take months to years to uncover such lineage relationships. We propose MalTree, a framework that applies bioinformatics-inspired phylogenetic techniques (UPGMA and Neighbor-Joining) at scale to model malware evolution automatically using structural, behavioral, and image-based features. We introduce temporal validation using VirusTotal timestamps to assess whether inferred trees reflect actual evolutionary order. MalTree achieves 87% temporal consistency, indicating that inferred evolutionary relationships closely align with real-world emergence timelines. Our analysis shows that some families mutate over 10 times faster than others, suggesting that detection strategies should be tailored to family-specific evolutionary tempos. Case studies, including the Mirai botnet, confirm that inferred relationships from our phylogenetic tree align with documented threat intelligence. Our framework provides a foundation for shifting malware analysis from sample-by-sample classification toward lineage-aware evolutionary modeling.
Attackers regularly use SSH (Secure SHell) to compromise systems, e.g., via brute-force attacks, establishing persistence by deploying SSH public keys. This ranges from IoT botnets like Mirai, over loader and dropper systems, to the back-ends of malicious operations. Identifying compromised systems at the Internet scale would be a major break-through for combatting malicious activity by enabling targeted clean-up efforts.
In this paper, we present a method to identify compromised SSH servers at scale. For this, we use SSH's behavior to only send a challenge during public key authentication, to check if the key is present on the system. Our technique neither allows us to access compromised systems (unlike, e.g., testing known attacker passwords), nor does it require access for auditing.
With our methodology used at an Internet-wide scan, we identify more than 21,700 unique systems (1,649 ASes, 144 countries) where attackers installed at least one of 52 verified malicious keys provided by a threat intelligence company, including critical Internet infrastructure. Furthermore, we find new context on the activities of malicious campaigns like, e.g., the 'fritzfrog' IoT botnet, malicious actors like 'teamtnt', and even the presence of state-actor associated keys within sensitive ASes. Comparing to honeypot data, we find these to under-/over-represent attackers' activity, even underestimating some APTs' activities. Finally, we collaborate with a national CSIRT and the Shadowserver Foundation to notify and remediate compromised systems. We run our measurements continuously and automatically share notifications. ...
In this paper, we present a method to identify compromised SSH servers at scale. For this, we use SSH's behavior to only send a challenge during public key authentication, to check if the key is present on the system. Our technique neither allows us to access compromised systems (unlike, e.g., testing known attacker passwords), nor does it require access for auditing.
With our methodology used at an Internet-wide scan, we identify more than 21,700 unique systems (1,649 ASes, 144 countries) where attackers installed at least one of 52 verified malicious keys provided by a threat intelligence company, including critical Internet infrastructure. Furthermore, we find new context on the activities of malicious campaigns like, e.g., the 'fritzfrog' IoT botnet, malicious actors like 'teamtnt', and even the presence of state-actor associated keys within sensitive ASes. Comparing to honeypot data, we find these to under-/over-represent attackers' activity, even underestimating some APTs' activities. Finally, we collaborate with a national CSIRT and the Shadowserver Foundation to notify and remediate compromised systems. We run our measurements continuously and automatically share notifications. ...
Attackers regularly use SSH (Secure SHell) to compromise systems, e.g., via brute-force attacks, establishing persistence by deploying SSH public keys. This ranges from IoT botnets like Mirai, over loader and dropper systems, to the back-ends of malicious operations. Identifying compromised systems at the Internet scale would be a major break-through for combatting malicious activity by enabling targeted clean-up efforts.
In this paper, we present a method to identify compromised SSH servers at scale. For this, we use SSH's behavior to only send a challenge during public key authentication, to check if the key is present on the system. Our technique neither allows us to access compromised systems (unlike, e.g., testing known attacker passwords), nor does it require access for auditing.
With our methodology used at an Internet-wide scan, we identify more than 21,700 unique systems (1,649 ASes, 144 countries) where attackers installed at least one of 52 verified malicious keys provided by a threat intelligence company, including critical Internet infrastructure. Furthermore, we find new context on the activities of malicious campaigns like, e.g., the 'fritzfrog' IoT botnet, malicious actors like 'teamtnt', and even the presence of state-actor associated keys within sensitive ASes. Comparing to honeypot data, we find these to under-/over-represent attackers' activity, even underestimating some APTs' activities. Finally, we collaborate with a national CSIRT and the Shadowserver Foundation to notify and remediate compromised systems. We run our measurements continuously and automatically share notifications.
In this paper, we present a method to identify compromised SSH servers at scale. For this, we use SSH's behavior to only send a challenge during public key authentication, to check if the key is present on the system. Our technique neither allows us to access compromised systems (unlike, e.g., testing known attacker passwords), nor does it require access for auditing.
With our methodology used at an Internet-wide scan, we identify more than 21,700 unique systems (1,649 ASes, 144 countries) where attackers installed at least one of 52 verified malicious keys provided by a threat intelligence company, including critical Internet infrastructure. Furthermore, we find new context on the activities of malicious campaigns like, e.g., the 'fritzfrog' IoT botnet, malicious actors like 'teamtnt', and even the presence of state-actor associated keys within sensitive ASes. Comparing to honeypot data, we find these to under-/over-represent attackers' activity, even underestimating some APTs' activities. Finally, we collaborate with a national CSIRT and the Shadowserver Foundation to notify and remediate compromised systems. We run our measurements continuously and automatically share notifications.
Current black-box backdoor attacks in convolutional neural networks formulate attack objective(s) as singleobjective optimization problems in single domain. Designing triggers in single domain harms semantics and trigger robustness as well as introduces visual and spectral anomaly. This work proposes a multi-objective black-box backdoor attack in dual domains via evolutionary algorithm (LADDER), the first instance of achieving multiple attack objectives simultaneously by optimizing triggers without requiring prior knowledge about victim model. In particular, we formulate LADDER as a multiobjective optimization problem (MOP) and solve it via multiobjective evolutionary algorithm (MOEA). MOEA maintains a population of triggers with trade-offs among attack objectives and uses non-dominated sort to drive triggers toward optimal solutions. We further apply preference-based selection to MOEA to exclude impractical triggers. LADDER investigates a new dualdomain perspective for trigger stealthiness by minimizing the anomaly between clean and poisoned samples in the spectral domain. Lastly, the robustness against preprocessing operations is achieved by pushing triggers to low-frequency regions. Extensive experiments comprehensively showcase that LADDER achieves attack effectiveness of at least 99%, attack robustness with 90.23% (50.09% higher than state-of-the-art attacks on average), superior natural stealthiness (1.12× to 196.74× improvement) and excellent spectral stealthiness (8.45× enhancement) as compared to current stealthy attacks by the average l2-norm across 5 public datasets.
...
Current black-box backdoor attacks in convolutional neural networks formulate attack objective(s) as singleobjective optimization problems in single domain. Designing triggers in single domain harms semantics and trigger robustness as well as introduces visual and spectral anomaly. This work proposes a multi-objective black-box backdoor attack in dual domains via evolutionary algorithm (LADDER), the first instance of achieving multiple attack objectives simultaneously by optimizing triggers without requiring prior knowledge about victim model. In particular, we formulate LADDER as a multiobjective optimization problem (MOP) and solve it via multiobjective evolutionary algorithm (MOEA). MOEA maintains a population of triggers with trade-offs among attack objectives and uses non-dominated sort to drive triggers toward optimal solutions. We further apply preference-based selection to MOEA to exclude impractical triggers. LADDER investigates a new dualdomain perspective for trigger stealthiness by minimizing the anomaly between clean and poisoned samples in the spectral domain. Lastly, the robustness against preprocessing operations is achieved by pushing triggers to low-frequency regions. Extensive experiments comprehensively showcase that LADDER achieves attack effectiveness of at least 99%, attack robustness with 90.23% (50.09% higher than state-of-the-art attacks on average), superior natural stealthiness (1.12× to 196.74× improvement) and excellent spectral stealthiness (8.45× enhancement) as compared to current stealthy attacks by the average l2-norm across 5 public datasets.
Endless Subscriptions
Open RAN is Open to RIC E2 Subscription Denial of Service Attacks
Conference paper
(2025)
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F. Klement, A. Brighente, A. K. Bhattacharjee, S. Cecconello, F. Kuipers, G. Smaragdakis, M. Conti, S. Katzenbeisser
Telecommunication services are essential in ensuring the operation of numerous critical infrastructures. While mobile network security increased with the advancement of generations, emerging concepts such as the Open Radio Access Network (O-RAN) are transforming the traditional operation of Radio Access Networks (RANs). Novel concepts and technologies are finding their way into RANs with a focus on softwareization and virtualization. This increases the overall attack surface and introduces new attack vectors not necessarily found in traditional RANs. This paper shows that Denial of Service (DoS) attacks leveraging subscription mechanisms can compromise O-RAN implementations. We present a novel DoS attack targeting the Near Real-Time (Near-RT) RAN Intelligent Controller (RIC). By deploying a malicious xApp, we demonstrate how an adversary can flood the Near-RT RIC with excessive subscription requests, leading to service disruption. This attack exploits the lack of rate-limiting mechanisms within the Service Model (SM), a critical component of the Near-RT RIC responsible for handling E2 subscription requests. We systematically evaluate various attack scenarios and investigate the underlying vulnerabilities exposed. Furthermore, we propose and assess countermeasures to safeguard publicly accessible O-RAN systems from such threats.
...
Telecommunication services are essential in ensuring the operation of numerous critical infrastructures. While mobile network security increased with the advancement of generations, emerging concepts such as the Open Radio Access Network (O-RAN) are transforming the traditional operation of Radio Access Networks (RANs). Novel concepts and technologies are finding their way into RANs with a focus on softwareization and virtualization. This increases the overall attack surface and introduces new attack vectors not necessarily found in traditional RANs. This paper shows that Denial of Service (DoS) attacks leveraging subscription mechanisms can compromise O-RAN implementations. We present a novel DoS attack targeting the Near Real-Time (Near-RT) RAN Intelligent Controller (RIC). By deploying a malicious xApp, we demonstrate how an adversary can flood the Near-RT RIC with excessive subscription requests, leading to service disruption. This attack exploits the lack of rate-limiting mechanisms within the Service Model (SM), a critical component of the Near-RT RIC responsible for handling E2 subscription requests. We systematically evaluate various attack scenarios and investigate the underlying vulnerabilities exposed. Furthermore, we propose and assess countermeasures to safeguard publicly accessible O-RAN systems from such threats.
Your PIN is Mine
Uncovering Users' PINs at Point of Sale Machines
Journal article
(2025)
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Stefano Cecconello, Matteo Cardaioli, Luca Pasa, Stjepan Picek, Georgios Smaragdakis
Point of Sale (PoS) machines have become extremely popular recently. In many economies, most transactions occur using them. Although PoS technology is evolving, PINs are still heavily used. In this paper, we perform a large-scale study to understand how difficult it is to uncover user PINs at PoS, even when the users cover the pad with their hands. Our study involves 142 participants, two types of PoS, and around 13,800 PINs. We develop machine learning techniques to infer PoS PINs by using hidden cameras. Our results show that uncovering PINs in PoS is more complex than in other cases where a user PIN is used, e.g., ATMs, because of the small pad area of PoS. Nevertheless, we could achieve more than 50% Top-3 accuracy for 4-digit PINs and 45% Top-3 accuracy for 5-digit PINs, even when the PIN is covered by the user's hand. We comment on the impact of the camera's position and PoS on the successful inference of the user's PINs. We also comment on the hardness of inferring PINs depending on the physical distance of digits and recommend what are good practices to generate PINs and cover PoS to make PIN inference difficult.
...
Point of Sale (PoS) machines have become extremely popular recently. In many economies, most transactions occur using them. Although PoS technology is evolving, PINs are still heavily used. In this paper, we perform a large-scale study to understand how difficult it is to uncover user PINs at PoS, even when the users cover the pad with their hands. Our study involves 142 participants, two types of PoS, and around 13,800 PINs. We develop machine learning techniques to infer PoS PINs by using hidden cameras. Our results show that uncovering PINs in PoS is more complex than in other cases where a user PIN is used, e.g., ATMs, because of the small pad area of PoS. Nevertheless, we could achieve more than 50% Top-3 accuracy for 4-digit PINs and 45% Top-3 accuracy for 5-digit PINs, even when the PIN is covered by the user's hand. We comment on the impact of the camera's position and PoS on the successful inference of the user's PINs. We also comment on the hardness of inferring PINs depending on the physical distance of digits and recommend what are good practices to generate PINs and cover PoS to make PIN inference difficult.
The rapid expansion of multi-cloud environments and the growing prevalence of collaborative data ownership present significant challenges in ensuring the verifiable deletion of co-owned data. Current approaches predominantly address individual ownership and often rely on simplistic one-bit result protocols where a deletion command merely outputs success or failure, turning the deletion into a black box without proper verification. This paper tackles the problem of secure processing and verifiable deletion of shared outsourced data in multi-cloud environments. We design a framework that enables a data owner to outsource encrypted data to multiple co-owners, who perform computations directly within their respective cloud providers---ensuring that sensitive data never leaves the cloud. Our system leverages readily available cloud Hardware Security Modules (HSMs) to manage cryptographic keys from generation to controlled destruction---ensuring data remains inaccessible beyond its intended use. Secure Enclaves enforce on-cloud data computation, eliminating local copies and preventing unauthorized exposure. Encrypted data is structured within a fixed storage model, ensuring controlled allocation and strict storage constraints. When data expires or must be deleted to meet regulatory requirements, our framework triggers zero-residual permuted overwriting to remove the data traces irreversibly. Verifiability is achieved at two levels: Bounded Merkle Hash Tree (BMHT) ensures bounded storage and verifiable deletion within each cloud provider. In contrast, Global Merkle Forest (GMF) aggregates BMHT roots across providers, enabling consistent global verification. The data owner maintains a log of these BMHT roots, allowing independent verification of secure deletion across the multi-cloud environment.
...
The rapid expansion of multi-cloud environments and the growing prevalence of collaborative data ownership present significant challenges in ensuring the verifiable deletion of co-owned data. Current approaches predominantly address individual ownership and often rely on simplistic one-bit result protocols where a deletion command merely outputs success or failure, turning the deletion into a black box without proper verification. This paper tackles the problem of secure processing and verifiable deletion of shared outsourced data in multi-cloud environments. We design a framework that enables a data owner to outsource encrypted data to multiple co-owners, who perform computations directly within their respective cloud providers---ensuring that sensitive data never leaves the cloud. Our system leverages readily available cloud Hardware Security Modules (HSMs) to manage cryptographic keys from generation to controlled destruction---ensuring data remains inaccessible beyond its intended use. Secure Enclaves enforce on-cloud data computation, eliminating local copies and preventing unauthorized exposure. Encrypted data is structured within a fixed storage model, ensuring controlled allocation and strict storage constraints. When data expires or must be deleted to meet regulatory requirements, our framework triggers zero-residual permuted overwriting to remove the data traces irreversibly. Verifiability is achieved at two levels: Bounded Merkle Hash Tree (BMHT) ensures bounded storage and verifiable deletion within each cloud provider. In contrast, Global Merkle Forest (GMF) aggregates BMHT roots across providers, enabling consistent global verification. The data owner maintains a log of these BMHT roots, allowing independent verification of secure deletion across the multi-cloud environment.
Trust but Verify
An Assessment of Vulnerability Tagging Services
Conference paper
(2025)
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Szu-Chun Huang, Harm Griffioen, Max van der Horst, Georgios Smaragdakis, Michel van Eeten, Yury Zhauniarovich
Internet-wide scanning services are widely used for attack surface discovery across organizations and the Internet. Enterprises, government agencies, and researchers rely on these tools to assess risks to Internet-facing infrastructure. However, their reliability and trustworthiness remain largely unexamined. This paper addresses this gap by comparing results from three commercial scanners – Shodan, ONYPHE, and LeakIX – with findings from our independent experiments using verified Nuclei templates, designed to identify specific vulnerabilities through crafted benign requests. We found that the payload based detections of Shodan are mostly confirmed. Yet, Nuclei finds many more vulnerable endpoints, so defenders might face massive underreporting. For Shodan’s banner-based detections, the opposite issue arises: a significant overreporting of false positives. This indicates that banner-based detections are unreliable. Moreover, three commercial services and Nuclei scans exhibit significant discrepancies. Our work has implications for industry users, policymakers, and the many academic researchers who rely on the results provided by these attack surface management services. By highlighting their shortcomings in vulnerability monitoring, this work serves as a call for action to advance and standardize such services to enhance their trustworthiness.
...
Internet-wide scanning services are widely used for attack surface discovery across organizations and the Internet. Enterprises, government agencies, and researchers rely on these tools to assess risks to Internet-facing infrastructure. However, their reliability and trustworthiness remain largely unexamined. This paper addresses this gap by comparing results from three commercial scanners – Shodan, ONYPHE, and LeakIX – with findings from our independent experiments using verified Nuclei templates, designed to identify specific vulnerabilities through crafted benign requests. We found that the payload based detections of Shodan are mostly confirmed. Yet, Nuclei finds many more vulnerable endpoints, so defenders might face massive underreporting. For Shodan’s banner-based detections, the opposite issue arises: a significant overreporting of false positives. This indicates that banner-based detections are unreliable. Moreover, three commercial services and Nuclei scans exhibit significant discrepancies. Our work has implications for industry users, policymakers, and the many academic researchers who rely on the results provided by these attack surface management services. By highlighting their shortcomings in vulnerability monitoring, this work serves as a call for action to advance and standardize such services to enhance their trustworthiness.
Conference paper
(2025)
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Yogesh Bhargav Suriyanarayanan, Cristian Munteanu, G. Smaragdakis, Anja Feldmann, Tobias Fiebig
Numerous studies have explored SSH attacks, often focusing on specific botnet activities or providing short-term analyses of particular honeynets. In this paper, we present an analysis of data collected from a large-scale honeynet over a three-year period, shedding light on gradual shifts in attacker behavior. Our findings suggest a trend toward more exploratory attacks, with indications that attackers are increasingly moving beyond the blind execution of scripts.
We observe changes in techniques as new bots appear with unique methods and established botnets modify their approaches over time. Furthermore, attackers have adopted a more scouting approach in recent months, showing increased adaptability in their tactics. Additionally, there is a clear preference for utilizing recently registered ASes as storage locations for malicious files. Our findings also suggest that attackers are increasingly aware of honeypot presence. Some attackers actively search for these traps, while others exploit honeypots for their own purposes, underscoring the need for a new generation of more advanced honeypots.
Lastly, we conduct a detailed investigation into one of the most prevalent attacks, challenging existing assumptions about the attacker's identity. ...
We observe changes in techniques as new bots appear with unique methods and established botnets modify their approaches over time. Furthermore, attackers have adopted a more scouting approach in recent months, showing increased adaptability in their tactics. Additionally, there is a clear preference for utilizing recently registered ASes as storage locations for malicious files. Our findings also suggest that attackers are increasingly aware of honeypot presence. Some attackers actively search for these traps, while others exploit honeypots for their own purposes, underscoring the need for a new generation of more advanced honeypots.
Lastly, we conduct a detailed investigation into one of the most prevalent attacks, challenging existing assumptions about the attacker's identity. ...
Numerous studies have explored SSH attacks, often focusing on specific botnet activities or providing short-term analyses of particular honeynets. In this paper, we present an analysis of data collected from a large-scale honeynet over a three-year period, shedding light on gradual shifts in attacker behavior. Our findings suggest a trend toward more exploratory attacks, with indications that attackers are increasingly moving beyond the blind execution of scripts.
We observe changes in techniques as new bots appear with unique methods and established botnets modify their approaches over time. Furthermore, attackers have adopted a more scouting approach in recent months, showing increased adaptability in their tactics. Additionally, there is a clear preference for utilizing recently registered ASes as storage locations for malicious files. Our findings also suggest that attackers are increasingly aware of honeypot presence. Some attackers actively search for these traps, while others exploit honeypots for their own purposes, underscoring the need for a new generation of more advanced honeypots.
Lastly, we conduct a detailed investigation into one of the most prevalent attacks, challenging existing assumptions about the attacker's identity.
We observe changes in techniques as new bots appear with unique methods and established botnets modify their approaches over time. Furthermore, attackers have adopted a more scouting approach in recent months, showing increased adaptability in their tactics. Additionally, there is a clear preference for utilizing recently registered ASes as storage locations for malicious files. Our findings also suggest that attackers are increasingly aware of honeypot presence. Some attackers actively search for these traps, while others exploit honeypots for their own purposes, underscoring the need for a new generation of more advanced honeypots.
Lastly, we conduct a detailed investigation into one of the most prevalent attacks, challenging existing assumptions about the attacker's identity.
Conference paper
(2025)
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Marie-Therese Sekwenz, Rita Gsenger, Volker Stocker, Esther Görnemann, Dinara Talypova, Simon Parkin, Lea Greminger, Georgios Smaragdakis
This paper investigates the feasibility and potential role of using Large Language Models (LLMs) to support systemic risk audits under the European Union’s Digital Services Act (DSA). It examines how automated tools can enhance the work of DSA auditors and other ecosystem actors by enabling scalable, explainable, and legally grounded content analysis. An interdisciplinary expert workshop with twelve participants from legal, technical, and social science backgrounds explored prompting strategies for LLM-assisted auditing. Thematic analysis of the sessions identified key challenges and design considerations, including prompt engineering, model interpretability, legal alignment, and user empowerment. Findings highlight the potential of LLMs to improve annotation workflows and expand audit scale, while underscoring the continued importance of human oversight, iterative testing, and cross-disciplinary collaboration. This study offers practical insights for integrating AI tools into auditing processes and contributes to emerging methodologies for operationalizing systemic risk evaluations under the DSA.
...
This paper investigates the feasibility and potential role of using Large Language Models (LLMs) to support systemic risk audits under the European Union’s Digital Services Act (DSA). It examines how automated tools can enhance the work of DSA auditors and other ecosystem actors by enabling scalable, explainable, and legally grounded content analysis. An interdisciplinary expert workshop with twelve participants from legal, technical, and social science backgrounds explored prompting strategies for LLM-assisted auditing. Thematic analysis of the sessions identified key challenges and design considerations, including prompt engineering, model interpretability, legal alignment, and user empowerment. Findings highlight the potential of LLMs to improve annotation workflows and expand audit scale, while underscoring the continued importance of human oversight, iterative testing, and cross-disciplinary collaboration. This study offers practical insights for integrating AI tools into auditing processes and contributes to emerging methodologies for operationalizing systemic risk evaluations under the DSA.
Clair Obscur
The Light and Shadow of System Call Interposition - From Pitfalls to Solutions with K23
Conference paper
(2025)
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Jesús María Gómez Moreno, Vissarion Moutafis, Antreas Dionysiou, Fernando Kuipers, Georgios Smaragdakis, Bart Coppens, Alexios Voulimeneas
System call interposition is a widely used technique to trace and modify application behavior. Over the years, numerous interposition mechanisms have been proposed, each with distinct strengths and trade-offs. Recently, advances in binary rewriting - specifically targeting x86-64 syscall and sysenter instructions - have led to new techniques that take important steps forward, with some claiming to support general-purpose use.We analyze state-of-the-art interposers in depth and uncover several fundamental design and implementation flaws - pitfalls that we collectively term System Call Interposition Pitfalls. For example, prior work cannot reliably interpose all system calls and may even corrupt code and data. These flaws undermine the practicality of existing solutions in real-world scenarios, rendering them unsuitable as universal interposition mechanisms.Motivated by our findings, we design and implement a new plug-and-play system call interposition approach named K23, targeting x86-64 platforms. K23 addresses the uncovered pitfalls via a hybrid design that unifies the strengths of prior methods, combining offline and online phases that leverage multiple Linux interfaces and binary rewriting. Our evaluation shows that K23 overcomes the key limitations of state-of-the-art solutions while remaining highly efficient. To our knowledge, K23 is the first general-purpose interposer suitable for a wide range of use cases and environments, from low-end devices to performance-critical, datacenter-scale workloads.
...
System call interposition is a widely used technique to trace and modify application behavior. Over the years, numerous interposition mechanisms have been proposed, each with distinct strengths and trade-offs. Recently, advances in binary rewriting - specifically targeting x86-64 syscall and sysenter instructions - have led to new techniques that take important steps forward, with some claiming to support general-purpose use.We analyze state-of-the-art interposers in depth and uncover several fundamental design and implementation flaws - pitfalls that we collectively term System Call Interposition Pitfalls. For example, prior work cannot reliably interpose all system calls and may even corrupt code and data. These flaws undermine the practicality of existing solutions in real-world scenarios, rendering them unsuitable as universal interposition mechanisms.Motivated by our findings, we design and implement a new plug-and-play system call interposition approach named K23, targeting x86-64 platforms. K23 addresses the uncovered pitfalls via a hybrid design that unifies the strengths of prior methods, combining offline and online phases that leverage multiple Linux interfaces and binary rewriting. Our evaluation shows that K23 overcomes the key limitations of state-of-the-art solutions while remaining highly efficient. To our knowledge, K23 is the first general-purpose interposer suitable for a wide range of use cases and environments, from low-end devices to performance-critical, datacenter-scale workloads.
Conference paper
(2025)
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Theodoros Apostolopoulos, Vasilios Koutsokostas, Nikolaos Totosis, Constantinos Patsakis, Georgios Smaragdakis
The continuous increase in malware samples, both in sophistication and number, presents many challenges for organizations and analysts, who must cope with thousands of new heterogeneous samples daily. This requires robust methods to quickly determine whether a file is malicious. Due to its speed and efficiency, static analysis is the first line of defense.
In this work, we illustrate how the practical state-of-the-art methods used by antivirus solutions may fail to detect evident malware traces. The reason is that they highly depend on very strict signatures where minor deviations prevent them from detecting shellcodes that otherwise would immediately be flagged as malicious. Thus, our findings illustrate that malware authors may drastically decrease the detections by converting the code base to less-used programming languages. To this end, we study the features that such programming languages introduce in executables and the practical issues that arise for practitioners to detect malicious activity. ...
In this work, we illustrate how the practical state-of-the-art methods used by antivirus solutions may fail to detect evident malware traces. The reason is that they highly depend on very strict signatures where minor deviations prevent them from detecting shellcodes that otherwise would immediately be flagged as malicious. Thus, our findings illustrate that malware authors may drastically decrease the detections by converting the code base to less-used programming languages. To this end, we study the features that such programming languages introduce in executables and the practical issues that arise for practitioners to detect malicious activity. ...
The continuous increase in malware samples, both in sophistication and number, presents many challenges for organizations and analysts, who must cope with thousands of new heterogeneous samples daily. This requires robust methods to quickly determine whether a file is malicious. Due to its speed and efficiency, static analysis is the first line of defense.
In this work, we illustrate how the practical state-of-the-art methods used by antivirus solutions may fail to detect evident malware traces. The reason is that they highly depend on very strict signatures where minor deviations prevent them from detecting shellcodes that otherwise would immediately be flagged as malicious. Thus, our findings illustrate that malware authors may drastically decrease the detections by converting the code base to less-used programming languages. To this end, we study the features that such programming languages introduce in executables and the practical issues that arise for practitioners to detect malicious activity.
In this work, we illustrate how the practical state-of-the-art methods used by antivirus solutions may fail to detect evident malware traces. The reason is that they highly depend on very strict signatures where minor deviations prevent them from detecting shellcodes that otherwise would immediately be flagged as malicious. Thus, our findings illustrate that malware authors may drastically decrease the detections by converting the code base to less-used programming languages. To this end, we study the features that such programming languages introduce in executables and the practical issues that arise for practitioners to detect malicious activity.
Industrial control systems have enabled the digitalization and automation of industrial production and services, such as electric powerhouses, the electric grid, and water supply networks. Due to their critical role, any exposure to the public Internet makes them vulnerable to attacks that may have catastrophic implications.In this paper, we report that the readily available application-layer scanning on all ports opens new avenues to assess the exposure of devices that run industrial control protocols that were not possible with previously proposed active port scanning. We consider 17 widely used industrial control system protocols and develop a methodology that unveils around 150 thousand industrial control systems exposed around the globe. Our study shows that many allegedly exposed industrial control systems are honeypots that emulate industrial protocols. Our methodology infers the presence of honeypots and classifies them into three tiers based on the confidence that these act as honeypots: low-, medium-, and high-confidence. We classify them thanks to large-scale application-layer scanning on all ports and multiple independent attributes, including network information, number of open ports, and known honeypot signatures. Our results show that 15% to 25% of the exposed industrial control systems are honeypots (with two-thirds of them belonging to the medium- or high-confidence categories). Our results challenge previous reports on the prevalence and distribution of exposed industrial control systems. The developed methodology enables industry operators to assess exposed assets and aid protection teams in creating stealthier honeypots.
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Industrial control systems have enabled the digitalization and automation of industrial production and services, such as electric powerhouses, the electric grid, and water supply networks. Due to their critical role, any exposure to the public Internet makes them vulnerable to attacks that may have catastrophic implications.In this paper, we report that the readily available application-layer scanning on all ports opens new avenues to assess the exposure of devices that run industrial control protocols that were not possible with previously proposed active port scanning. We consider 17 widely used industrial control system protocols and develop a methodology that unveils around 150 thousand industrial control systems exposed around the globe. Our study shows that many allegedly exposed industrial control systems are honeypots that emulate industrial protocols. Our methodology infers the presence of honeypots and classifies them into three tiers based on the confidence that these act as honeypots: low-, medium-, and high-confidence. We classify them thanks to large-scale application-layer scanning on all ports and multiple independent attributes, including network information, number of open ports, and known honeypot signatures. Our results show that 15% to 25% of the exposed industrial control systems are honeypots (with two-thirds of them belonging to the medium- or high-confidence categories). Our results challenge previous reports on the prevalence and distribution of exposed industrial control systems. The developed methodology enables industry operators to assess exposed assets and aid protection teams in creating stealthier honeypots.
Network telescopes have been utilized for decades to detect scanning activity on the Internet. Such telescopes are typically passive, i.e., they do not reply to TCP SYN packets. Recently, reactive network telescopes that respond to TCP SYN packets have been proposed to unveil a new wave of scanners, namely two-phase scanners, and collect malicious payloads from TCP ACK packets. In this paper, we propose a methodology that combines the modus operandi of passive and reactive telescopes to identify an additional wave of scanners - that we call “informed scanners"that participate in attacks. Our main observation is that small reactive telescopes operating within larger passive telescopes are visited by “informed” clients that are aware of the liveness of hosts without performing scanning themselves; thus, are not visible in the passive telescope. We identify these informed clients as an additional class of highly targeted scanners and attackers. Indeed, by operating a /25 reactive telescope within a /16 passive telescope, we can filter out routine and two-phase scanning activity from informed one and identify clients that participate in service-targeted attacks. We discuss the scalability and sensitivity of our methodology and how it can be used to swiftly identify and profile malicious hosts on the Internet. We show that “mini-telescopes” of relatively smaller sizes, such as /20, can be comparably effective as larger sizes, such as a /16. Thus, our methodology can be useful to security operators that may only be able to allocate a relatively small address space to run a telescope.
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Network telescopes have been utilized for decades to detect scanning activity on the Internet. Such telescopes are typically passive, i.e., they do not reply to TCP SYN packets. Recently, reactive network telescopes that respond to TCP SYN packets have been proposed to unveil a new wave of scanners, namely two-phase scanners, and collect malicious payloads from TCP ACK packets. In this paper, we propose a methodology that combines the modus operandi of passive and reactive telescopes to identify an additional wave of scanners - that we call “informed scanners"that participate in attacks. Our main observation is that small reactive telescopes operating within larger passive telescopes are visited by “informed” clients that are aware of the liveness of hosts without performing scanning themselves; thus, are not visible in the passive telescope. We identify these informed clients as an additional class of highly targeted scanners and attackers. Indeed, by operating a /25 reactive telescope within a /16 passive telescope, we can filter out routine and two-phase scanning activity from informed one and identify clients that participate in service-targeted attacks. We discuss the scalability and sensitivity of our methodology and how it can be used to swiftly identify and profile malicious hosts on the Internet. We show that “mini-telescopes” of relatively smaller sizes, such as /20, can be comparably effective as larger sizes, such as a /16. Thus, our methodology can be useful to security operators that may only be able to allocate a relatively small address space to run a telescope.
Bitcoin Battle
Burning Bitcoin for Geopolitical Fun and Profit
This study empirically analyzes the transaction activity of Bitcoin addresses linked to Russian intelligence services, which have liquidated over 7 Bitcoin (BTC), i.e., equivalent to approximately US$300,000 based on the exchange rate at the time. Our investigation begins with an observed anomaly in transaction outputs featuring the Bitcoin Script OP_RETURN operation code, tied to input addresses identified by cyber threat intelligence sources and court documents as belonging to Russian intelligence agencies. We explore how an unauthorized entity appears to have gained control of the associated private keys, with messages embedded in the OP_RETURN outputs confirming the seizure. Tracing the funds' origins, we connect them to cryptocurrency mixers and establish a link to the Russian ransomware group Conti, implicating intelligence service involvement. This analysis represents one of the first empirical studies of large-scale Bitcoin misuse by nation-state cyber actors.
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This study empirically analyzes the transaction activity of Bitcoin addresses linked to Russian intelligence services, which have liquidated over 7 Bitcoin (BTC), i.e., equivalent to approximately US$300,000 based on the exchange rate at the time. Our investigation begins with an observed anomaly in transaction outputs featuring the Bitcoin Script OP_RETURN operation code, tied to input addresses identified by cyber threat intelligence sources and court documents as belonging to Russian intelligence agencies. We explore how an unauthorized entity appears to have gained control of the associated private keys, with messages embedded in the OP_RETURN outputs confirming the seizure. Tracing the funds' origins, we connect them to cryptocurrency mixers and establish a link to the Russian ransomware group Conti, implicating intelligence service involvement. This analysis represents one of the first empirical studies of large-scale Bitcoin misuse by nation-state cyber actors.
Databases often store sensitive organizational data but may be exposed to the Internet through misconfiguration or vulnerabilities. However, such databases may be unintentionally exposed to the Internet, e.g., due to misconfiguration or be vulnerable. To study real-world attacks on public-facing database management systems (DBMS), we deployed 278 honeypots over 20 days in March–April 2024. Our 220 low-interaction honeypots emulate MySQL, MSSQL, PostgreSQL, and Redis, revealing that scanning activity is relatively low (?3,000 IPs), but brute-force attempts are persistent. We also deploy 58 medium/high-interaction honeypots, which reveal three distinct types of exploitation: (i) direct attacks on the database management system to manipulate the database, (ii) ransom-driven attacks that copy and delete the targeted data, and (iii) use the database as an attack vector to take over the underlying system. Our findings highlight that DBMS-targeted attacks are distinct from those on other Internet-facing systems and deserve focused attention.
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Databases often store sensitive organizational data but may be exposed to the Internet through misconfiguration or vulnerabilities. However, such databases may be unintentionally exposed to the Internet, e.g., due to misconfiguration or be vulnerable. To study real-world attacks on public-facing database management systems (DBMS), we deployed 278 honeypots over 20 days in March–April 2024. Our 220 low-interaction honeypots emulate MySQL, MSSQL, PostgreSQL, and Redis, revealing that scanning activity is relatively low (?3,000 IPs), but brute-force attempts are persistent. We also deploy 58 medium/high-interaction honeypots, which reveal three distinct types of exploitation: (i) direct attacks on the database management system to manipulate the database, (ii) ransom-driven attacks that copy and delete the targeted data, and (iii) use the database as an attack vector to take over the underlying system. Our findings highlight that DBMS-targeted attacks are distinct from those on other Internet-facing systems and deserve focused attention.
Conference paper
(2024)
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Antonios Chatzivasileiou, Alexandros Kornilakis, Katerina Lionta, Georgios Nomikos, Xenofontas Dimitropoulos, Georgios Smaragdakis
On February 24, 2022, Russia invaded Ukraine after months of military preparations. Although secondary to the human tragedy resulting from the war, the Internet connectivity in the region was disrupted due to the military conflicts and economic sanctions. We study the Internet peering connectivity of the conflicted countries before, during, and after the Russian invasion of Ukraine. Our analysis shows that de-peering activity by Ukrainian, Russian, and international networks started months before the invasion at peering facilities in Ukraine and Russia, respectively. De-peering continued after the Russian invasion of Ukraine, with only minor changes in peering taking place until end of 2023. Our study shows that several Internet exchange points have stopped operating in Ukraine. We also report that the invasion has impacted the registry country code of operational networks in Ukraine and Russia, creating a new status quo in Internet peering in the region.
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On February 24, 2022, Russia invaded Ukraine after months of military preparations. Although secondary to the human tragedy resulting from the war, the Internet connectivity in the region was disrupted due to the military conflicts and economic sanctions. We study the Internet peering connectivity of the conflicted countries before, during, and after the Russian invasion of Ukraine. Our analysis shows that de-peering activity by Ukrainian, Russian, and international networks started months before the invasion at peering facilities in Ukraine and Russia, respectively. De-peering continued after the Russian invasion of Ukraine, with only minor changes in peering taking place until end of 2023. Our study shows that several Internet exchange points have stopped operating in Ukraine. We also report that the invasion has impacted the registry country code of operational networks in Ukraine and Russia, creating a new status quo in Internet peering in the region.
Malware is recognized as one of the most severe cybersecurity threats today. Although malware attacks are as old as the Internet, our understanding of which part of the Internet infrastructure is used to distribute malware software is still rather limited.
In this work, we analyze more than 3 million sessions established with honeypots deployed in 55 countries that are associated with the download and execution of malware binaries. We identify two main tactics to load malware to infected machines: injection of malware by hosts initiating the connection (clients) and downloading malware from third parties (loaders). The latter tactic contributes to more than 80% of this class of sessions but involves a smaller number of cloud and content delivery servers with very different profiles than that of the clients. Our analysis also shows that it is not uncommon for different malware families to rely on the same hosting infrastructures for downloading malware. Further investigation into the code executed to download and activate malware shows that criminals tend to hide their traces by deleting their history and modifying logs and files on the compromised machines. ...
In this work, we analyze more than 3 million sessions established with honeypots deployed in 55 countries that are associated with the download and execution of malware binaries. We identify two main tactics to load malware to infected machines: injection of malware by hosts initiating the connection (clients) and downloading malware from third parties (loaders). The latter tactic contributes to more than 80% of this class of sessions but involves a smaller number of cloud and content delivery servers with very different profiles than that of the clients. Our analysis also shows that it is not uncommon for different malware families to rely on the same hosting infrastructures for downloading malware. Further investigation into the code executed to download and activate malware shows that criminals tend to hide their traces by deleting their history and modifying logs and files on the compromised machines. ...
Malware is recognized as one of the most severe cybersecurity threats today. Although malware attacks are as old as the Internet, our understanding of which part of the Internet infrastructure is used to distribute malware software is still rather limited.
In this work, we analyze more than 3 million sessions established with honeypots deployed in 55 countries that are associated with the download and execution of malware binaries. We identify two main tactics to load malware to infected machines: injection of malware by hosts initiating the connection (clients) and downloading malware from third parties (loaders). The latter tactic contributes to more than 80% of this class of sessions but involves a smaller number of cloud and content delivery servers with very different profiles than that of the clients. Our analysis also shows that it is not uncommon for different malware families to rely on the same hosting infrastructures for downloading malware. Further investigation into the code executed to download and activate malware shows that criminals tend to hide their traces by deleting their history and modifying logs and files on the compromised machines.
In this work, we analyze more than 3 million sessions established with honeypots deployed in 55 countries that are associated with the download and execution of malware binaries. We identify two main tactics to load malware to infected machines: injection of malware by hosts initiating the connection (clients) and downloading malware from third parties (loaders). The latter tactic contributes to more than 80% of this class of sessions but involves a smaller number of cloud and content delivery servers with very different profiles than that of the clients. Our analysis also shows that it is not uncommon for different malware families to rely on the same hosting infrastructures for downloading malware. Further investigation into the code executed to download and activate malware shows that criminals tend to hide their traces by deleting their history and modifying logs and files on the compromised machines.
Orbital Shield
Rethinking Satellite Security in the Commercial Off-the-Shelf Era
Conference paper
(2024)
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Nikita Yadav, Franziska Vollmer, Ahmad Reza Sadeghi, Georgios Smaragdakis, Alexios Voulimeneas
Satellites have become part of critical infrastructure utilized for diverse applications, from Earth observation to communication and military missions. Several trends have reshaped satellite deployment and utilization in recent years, making satellite systems more accessible and vulnerable to cybersecurity threats. A notable trend is adopting Commercially Off-the-Shelf (COTS) hardware and software for satellite systems. However, this approach renders satellites susceptible to well-known cyberattacks. This paper presents a comprehensive exploration of attacks on satellite systems, with a specific emphasis on the security aspects of the satellite platform, encompassing both the bus and payload subsystems. The discussion includes existing security defenses that can enhance the security of the satellite platform. Ultimately, we present a real-world security framework designed to improve the overall security of the satellite platform.
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Satellites have become part of critical infrastructure utilized for diverse applications, from Earth observation to communication and military missions. Several trends have reshaped satellite deployment and utilization in recent years, making satellite systems more accessible and vulnerable to cybersecurity threats. A notable trend is adopting Commercially Off-the-Shelf (COTS) hardware and software for satellite systems. However, this approach renders satellites susceptible to well-known cyberattacks. This paper presents a comprehensive exploration of attacks on satellite systems, with a specific emphasis on the security aspects of the satellite platform, encompassing both the bus and payload subsystems. The discussion includes existing security defenses that can enhance the security of the satellite platform. Ultimately, we present a real-world security framework designed to improve the overall security of the satellite platform.