As SDN becomes the backbone of today's cloud and enterprise infrastructure, its security evaluation remains scattered and inconsistent. A persistent gap exists in current frameworks regarding transparently and specifically assessing the impact of SDN attacks and the effectiveness of corresponding defenses. General-purpose tools like CVSS overlook critical SDN characteristics-scalability, vendor independence, resource constraints, and operational visibility-essential for measuring impact. Platform-specific systems (e.g., ONOS, OpenDaylight) suffer from inconsistent criteria, limited cross-vendor applicability, and opaque scoring processes. Moreover, many impactful SDN attacks remain untracked in CVE databases due to disclosure barriers and the opaque nature of the SDN ecosystem. To address these gaps, we present Odin, an open-source, lightweight framework for evaluating SDN security in cloud and enterprise environments. Odin scores attack impact and defense effectiveness across three dimensions: technical severity, resource feasibility, and operational visibility. Applied to 20 real-world attack-defense scenarios-including underreported cases-Odin offers more contextualized prioritization and actionable insights than CVSS. Our results show that Odin scores align with or exceed CVSS ratings for all evaluated attacks, while also capturing defenses' effectiveness and operational trade-offs. By providing transparency, tunability, and SDN-specific relevance, Odin helps researchers and practitioners assess risk and improve resilience across varied deployment environments.
With the growing adoption of cloud computing services, outsourced machine learning has become increasingly prevalent, raising significant concerns about the privacy of sensitive data. Users are often reluctant to reveal their personal information to access these services, while model providers strive to protect the security of their proprietary models. Naive Bayes classification is a widely used method for probabilistic decision-making in applications such as spam filtering, text categorization, and medical diagnosis. When either model parameters or user inputs are outsourced to the cloud, ensuring data security becomes essential. In this work, we present FssNBC, a secure and online efficient outsourced Naive Bayes classification protocol based on function secret sharing. FssNBC adopts an offline-online computation model that shifts most cryptographic operations to the offline phase, achieving both lightweight online cost and strong security guarantees. We implement and evaluate FssNBC, demonstrating its advantages over existing solutions in terms of both computational and communication efficiency. Compared to existing two-server protocols offering equivalent security guarantees, FssNBC improves online computation time by 10(8)x and reduces online communication cost by 10(3)x, making it highly practical for real-world deployment.
At Eurocrypt 2013, Bellare, Keelveedhi, and Ristenpart initiated a rigorous study of the important, practically-motivated problem of "Secure Deduplication". Deduplication deals with storing data efficiently by storing duplicate data only once; when data is spread across multiple parties, ensuring privacy becomes an added concern. Even though deduplication and privacy might appear to be mutually incompatible, Bellare et al. offer a meaningful definition as well as theoretical and practical constructions for the same. Our work considers the setting of Secure Fuzzy Deduplication where deduplication of nearby-and not equal-files is desired. We provide a rigorous definition of fuzzy message locked encryption as well as fuzzy deduplication. We build constructions that satisfy our definitions in the random oracle model and provide proof-of-concept implementations of the same by instantiating our random oracle using AES. Finally, we propose a general paradigm of data-adaptive clustering, where the clusters, which are intrinsic to fuzzy deduplication, are created based on the data. We believe this paradigm yields better deduplication efficiency for typical use cases involving time-series data, such as medical images (e.g., X-ray scans taken over time), CCTV footage from secure locations, DNA sequencing datasets, and longitudinal studies that collect large volumes of data (e.g., voice samples, videos capturing subject movement).
WebAssembly has become increasingly popular in web development, offering a versatile and efficient platform for executing code in languages beyond JavaScript, such as C and C++. However, WebAssembly's flat memory model exposes memory-safety vulnerabilities. While C and C++ code are primarily susceptible to memory safety issues, Rust, though improved, still presents vulnerabilities[44]. Inspired by the ARM Memory Tagging Extension, this paper proposes a memory tagging solution for WebAssembly. Our evaluation indicates that the proposed memory tagging mechanism introduces average time overheads of 48.91% for Wasm64 and 72.38% for Wasm32 in pure software implementations. On ARM Memory Tagging Extension-supported CPUs, the time overheads decrease to 5.71% for Wasm64 and 18.05% for Wasm32. Additionally, we compare our WebAssembly memory tagging to the host Address Sanitizer to demonstrate the efficacy of our approach. Finally, we conduct a case study on real-world CVEs to demonstrate the impact of our work.
The increasing complexity of cloud-native applications has necessitated advanced methodologies for threat modeling and security analysis. This paper presents THREATCOMPUTE, a novel framework that combines LLMs with attack graphs to automate the generation of threat hypotheses and the quantification of risk in Kubernetes environments. While traditional approaches to attack graph generation require significant manual effort from security experts, THREATCOMPUTE leverages LLMs to extract security insights from system information, reducing reliance on manual intervention while maintaining high accuracy and generating context-specific, system-aware threat insights. The framework utilizes the MITRE ATT&CK Matrix and the Microsoft Threat Matrix for Kubernetes as structured domains of possible attack techniques. Based on LLM-generated threat hypotheses and a quantitative risk metric, THREATCOMPUTE constructs detailed attack graphs that illustrate potential attack paths and assess their associated risks. This enables both qualitative and quantitative evaluations of application security across varying levels of granularity. Through real-world examples of Kubernetes applications, we demonstrate the effectiveness of our approach in identifying and quantifying security risks.
Cloud-native environments are increasingly vulnerable to lateral movement attacks that exploit misconfigured or vulnerable containers. Traditional service meshes rely on static configurations and lack runtime adaptability, while conventional NIDS operate globally, introducing latency and failing to reflect the behavioral patterns specific to containerized services. To address these limitations, we propose a service mesh with integrated NIDS, implemented in Kubernetes. Each application container is paired with a lightweight proxy that intercepts traffic and performs real-time anomaly detection using a lightweight CNN encoder obtained through knowledge distillation from a self-supervised teacher model. Combined with a One-Class SVM, the system enables unsupervised, service-specific detection from unlabeled benign traffic. We evaluate our approach using real-world microservice deployments and MITRE ATT&CK-based lateral movement scenarios. Experimental results show that the proposed KD-CNN+OCSVM model outperforms existing deep learning methods in detection accuracy, while knowledge distillation reduces inference latency by up to 88.74% with minimal degradation in performance. Even with detection enabled, the system maintains end-to-end latency below 14 ms and throughput above 600 req/s, indicating its suitability for deployment in resource-constrained cloud-native environments.
Software supply chain security is an essential area of cybersecurity, as shown by attacks such as the XZ Utils incident in 2024. Software Bills of Materials (SBOMs) were introduced to keep track of the supply chain of a software artifact. They have been brought to widespread attention in 2021 through US Executive Order 14028. The EU will make SBOMs mandatory by 2027 with their Cyber Resilience Act from 2024. In contrast to these demands for SBOMs through legislation, practitioners struggle to make real use of them. Multiple recent studies have concluded that adoption of SBOMs and their integration into security processes are facing various challenges. At the same time, accuracy and correctness problems of SBOMs generated by wide-spread tools have been shown. Based on this situation, the dissertation research is dedicated to the research question: What benefit do SBOMs provide and what further benefit can they potentially provide for supply chain security? In order to answer, this research description introduces four intended contributions: (1) First, all knowledge about the history of SBOMs and related concepts in Computer Science is systematized. (2) Second, an SBOM usage model that provides an abstract view on the software supply chain as well as actors involved in it is developed. The goal of the model is to give a theoretical foundation for making practical use of SBOMs. (3) Third, with BOM2VULN an analysis of current tools that map vulnerabilities to SBOMs is conducted, potentially including the introduction of a new tool for this task. The goal is to provide security engineers with a means of quickly finding vulnerabilities and assessing their relevance. (4) Lastly, the SBOM Nutri Score is proposed including an evaluation. The score helps practitioners to evaluate the software supply chain risk of third party code they use or intend to use.
Man-At-The-End (MATE) attackers operate with full access to software or hardware targets and can observe, analyze, and modify running systems to extract secrets or alter behavior. CheckMATE explores both offensive and defensive research in this space: measurement studies and tooling that expose realistic attack techniques, alongside defenses such as obfuscation, tamper-resistance, watermarking, white-box cryptography, and hardware-assisted protections. This workshop collects rigorous, reproducible research aimed at bridging academic advances and industry practice. The CheckMATE '25 complete workshop proceedings can be found at: https://dl.acm.org/citation.cfm?id=3733817
Most homomorphic encryption (FHE) schemes exploit a technique called single-instruction multiple-data (SIMD) to process several messages in parallel. However, they base their security in somehow strong assumptions, such as the hardness of approximate lattice problems with superpolynomial approximation factor. On the other extreme of the spectrum, there are lightweight FHE schemes that have much faster bootstrapping but no SIMD capabilities. On the positive side, the security of these schemes is based on lattice problems with (low-degree) polynomial approximation factor only, which is a much weaker security assumption. Aiming the best of those two options, Micciancio and Sorrell (ICALP'18) proposed a new amortized bootstrapping that can process many messages at once, yielding sublinear time complexity per message, and allowing one to construct FHE based on lattice problems with polynomial approximation factor. Some subsequent works on this line achieve near-optimal asymptotic performance, nevertheless, concrete efficiency remains mostly an open problem. The only existing implementation to date (GPV23, Asiacrypt 2023) requires keys of up to a hundred gigabytes while only providing gains for relatively large messages. In this paper, we introduce a new method for amortized bootstrapping where the number of homomorphic operations required per message is O(h) and the noise overhead is O(root h lambda log lambda), where h is the Hamming weight of the LWE secret key and lambda is the security parameter. This allows us to use much smaller parameters and to obtain faster running time. Our method is based on a new efficient homomorphic evaluation of sparse polynomial multiplication. We bootstrap 2 to 8-bit messages in 1.46 ms to 28.5 ms, respectively. Compared to TFHE-rs, this represents a performance improvement of 2.5 to 38.7 times while requiring bootstrapping keys up to 47.5 times smaller.
The widespread adoption of Large Language Models (LLMs) is driving the rapidly growing demand for large-scale computations like training and fine-tuning models. In many areas, the confidentiality of the underlying data is of critical importance to their corporate or government owners. However, securing data in large-scale computations is challenging. First, its demand for enormous hardware resources typically requires outsourcing (e.g., to the public cloud). Second, the large and rapidly evolving software stack used in LLM training in conjunction with a growing incidence of supply chain attacks and software vulnerabilities makes it all but impossible for data owners to establish trust in the code that processes their highly sensitive data. Confidential computing and sandboxing are promising techniques for solving these problems. However, existing sandboxes do not address covert channels which limits their ability to protect confidential data. This paper proposes IOVALVE, a novel I/O sandbox for large-scale computations on confidential data. IOVALVE places sandbox enforcement on a programmable network device that is physically isolated from the processor hardware running the untrusted software stack. This construction allows IOVALVE to sidestep the multitude of side channels due to visible or hidden resource sharing. IOVALVE interposes on all network I/O of the sandbox and only transmits encrypted and regularized network traffic in order to prevent information leakage over the network. Our evaluation shows that IOVALVE has marginal performance overhead and supports real-world applications like LLM fine-tuning and batch inference, and molecular simulation.
As the de-facto standard for container orchestration, Kubernetes is extensively adopted by numerous companies and cloud vendors, making its security critical. In this paper, we define a new attack surface called implicit permission: The execution of explicitly granted permissions in Kubernetes dynamically leads to implicit operations on other resources, enabling new permissions beyond the explicitly granted ones. Such implicit permissions create security vulnerabilities that attackers can exploit to compromise an entire cluster. Automatically identifying implicit permissions is challenging due to implicit relation reasoning and dynamic behaviors across diverse components of Kubernetes. To address that, we devise a systematic approach that combines static analysis techniques with the advanced capabilities of the large language model (LLM, e.g., GPT-4.5). Initially, we develop a static analysis to identify all Kubernetes resources. Building on this, we use static analysis to identify all explicit permissions for each resource. Finally, by combining the semantic reasoning capabilities of LLMs with the pattern-based precision of static analysis, we reason about what explicit permissions may dynamically lead to implicit permissions through complex interactions and uncover 593 implicit permissions derived from explicit permissions. We use the implicit permission references as insights to identify potential risks of CNCF projects and applications provided by the top four cloud vendors. With responsible disclosure, we obtain five new CVEs, six acknowledgments of cloud vendors, and a bounty awarded by Google. These acknowledgments underlie the practical impact of our attack.
The ARTMAN workshop aims to bring together academic researchers and industry practitioners from diverse domains, primarily security & privacy and machine learning, but also various application fields, to collaboratively explore and discuss resilient and trustworthy machine learning-powered applications and systems. This workshop focuses on AI/ML application domains and welcomes contributions on both foundational and applied aspects of ML across various industries, including transportation, aerospace, healthcare, energy, and finance, among others, showcasing AI-driven advances in performance and efficiency. This workshop also seeks contributions on the application of reliable and secure AI/ML algorithms, especially knowledge-informed approaches, to improve resilience and trust, particularly in human-machine partnerships and interactions within such scenarios.
Deep learning techniques are increasingly being incorporated into NIDS. However, the evaluation of such deep learning models often assumes static data distributions and overlooks the effects of randomness and environmental variation. As a result, the reported performance may not reflect the NIDS behaviour during real-world deployment. This paper investigates the impact of stochastic and environmental factors on the evaluation of deep learning models for NIDS, with a focus on shift-aware models that detect and adapt to data shift, representing state-of-the-art systems for long-term deployment. We examine two baselines under controlled variations to analyse the impact of each factor on the reproducibility and fairness of the results, revealing that the F-1 score can vary largely due to these, even minor, variations. All of the explored factors affect the reproducibility of the results, and some can significantly skew performance. Based on our findings, we provide practical recommendations to support reproducible and fair evaluations of deep learning-based NIDS systems.
The use of biometric-based security protocols is on the steep rise. As biometrics become more popular, we witness more attacks. For example, recent BrutePrint/InfinityGauntlet attacks showed how to brute-force fingerprints stored on an Android phone in about 40 minutes. The attacks are possible because biometrics, like passwords, do not have high entropy. But unlike passwords, brute-force attacks are much more damaging for biometrics, because one cannot easily change biometrics in case of compromise. In this work, we propose a novel provably secure Brute-Force Resistant Biometrics (BFRB) protocol for biometric-based authentication and key reconstruction that protects against brute-force attacks even when the server storing biometric-related data is compromised. Our protocol utilizes a verifiable partially oblivious pseudorandom function, an authenticated encryption scheme, a pseudorandom function, and a hash. We formally define security for a BFRB protocol and reduce the security of our protocol to the security of the building blocks. We implement the protocol and study its performance for the ND-0405 iris dataset.
We present SlicedPIR, a distributed Private Information Retrieval (PIR) protocol. SlicedPIR efficiently alleviates the server's compute bottleneck by offloading its load across multiple untrusted client machines. In contrast to prior work, SlicedPIR induces only a modest network overhead when the server offloads its work. It achieves those communication savings by exploiting the polynomial encoding of homomorphic encryption schemes typically used in PIR protocols. This encoding lets the server make novel use of the Number Theoretic Transform (NTT) to distribute points on the polynomials as "slices" of its data rather than the polynomials themselves. Using NTT allows the clients to process recursive PIR queries on their slices and return a succinct result to the server. The server efficiently verifies the clients' results by leveraging the Schwartz-Zippel lemma, which we adapt to the PIR use case. We show how to integrate SlicedPIR into a private messaging system, where clients write messages to the server's database and then use PIR to secretly query for messages from their friends. We implement a prototype of SlicedPIR and run experiments to show that it scales well with the number of clients and database size. Concretely, SlicedPIR achieves better performance and cuts network usage by over 95% compared to the state-of-the-art.
Extended Reality (XR) headsets are increasingly serving as repositories for substantial volumes of sensitive data and gateways to web applications. This transition highlights the need for convenient and secure user authentication solutions. Traditional password/PIN-based schemes are ill-suited to the XR's gesture- and voice-based interfaces and are prone to shoulder-surfing attacks. Some recent XR systems incorporate two-factor authentication, but it requires additional operations on a second device (e.g., a smartphone or wearable). In this work, we introduce the first effortless and inbuilt XR user authentication system by leveraging the harmonics of vibrations excited by users' vital signs. The system is transparent to users (no efforts during enrollment and authentication) and requires no additional hardware. The key idea is that vital signs (i.e., breathing and heart beating) naturally generate low-frequency mechanical vibrations, causing human skull to vibrate and produces harmonic signals. When the harmonics pass the human head, they carry rich biometrics associated with the wearer's skull structure and soft tissues, which can be captured by the XR motion sensors. Instead of directly utilizing the vibrations, we extract more reliable biometrics from the ratios among different harmonic frequencies, which capture wearers' unique head and facial attenuation properties and are non-volatile when the periodicity and amplitude of vital signs fluctuate. We further design an adaptive filter to mitigate the body motion distortions in common XR interactions. By adopting advanced deep learning models with the attention mechanism, our system realizes effective and robust authentication across XR scenarios. Evaluations across 10 months, with 52 users and two popular XR headsets, show that our system can accurately authenticate users with over 95% true positive rates and rejects unauthorized users with over 98% true negative rates under various XR scenarios, with biometrics remaining consistent over long-term periods.
Industrial Control System (ICS) and its software touches every aspect of the critical infrastructure used by our industry, academia, and government. Back in the days, these systems and software were not designed with security in mind. With the ever expanding interconnectivity of ICS environments and new threats, practitioners are stuck on a patchwork of security. While certain proprietary ICS software manufacturers have started to provide security solutions, free and open source ICS software is often less known. The goal of the workshop is twofold: we want to collect ideas on redesigning (parts of) the ICS ecosystem so that security is built-in by design; we also invite contributions on designing, incorporating, and maintaining secure open-source ICS software. The RICCS '25 complete workshop proceedings can be found at: https://dl.acm.org/citation.cfm?id=3733823
Homomorphic encryption (HE) is a foundational technology in privacy-enhancing cryptography, enabling computation over encrypted data. Recently, generalized HE primitives designed for multi-party applications, such as multi-party HE (MPHE), have garnered significant research interest. While constructing secure multi-party protocols from MPHE in the semi-honest model is straightforward, achieving malicious security remains challenging as it requires zero-knowledge arguments of knowledge (ZKAoKs) for MPHE ciphertexts and public keys. In this work, we design practical ZKAoKs for MPHE that validate the well-formedness of public keys and ciphertexts. Specifically, we develop our ZKAoKs within the polynomial interactive oracle proof (PIOP) framework. To achieve this, we introduce novel optimization techniques that seamlessly integrate constraints for MPHE into the PIOP framework, enabling the design of PIOPs for validating all types of MPHE public keys, including relinearization and automorphism keys. To the best of our knowledge, our construction is the first ZKAoK for MPHE that validates automorphism keys. We instantiate our PIOP using a lattice-based polynomial commitment scheme (PCS). When compared with the previous state-of-the-art construction, PELTA (ACM CCS 2023), our implementation achieves a 5.4x reduction in proof size, a 111x speed-up in proof generation, and a 768x improvement in verification time for validating the encryption key. In addition to the encryption key, we provide benchmark results for all types of ZKAoKs required for MPHE, presenting the first concrete performance results in compiling passively secure MPHE-based protocols into maliciously secure ones.
Mobile super apps, which bundle multiple mini-apps into a single platform, have become central to the consumer-facing digital ecosystem. Services such as WeChat, Alipay, Grab, and TikTok integrate payments, messaging, commerce, and entertainment, while at the same time collecting and processing large volumes of sensitive personal data. This concentration of functionality creates unprecedented opportunities to businesses and online services but also raises significant security and privacy risks. Meanwhile, a growing trend is the integration of large language models (LLMs) into mobile apps, transforming them into LLM-driven agentic systems. These systems are capable of orchestrating mini-apps and other mobile apps, interacting with external services, and carrying out privileged tasks on behalf of users. While this enables powerful new applications, it also expands the attack surface and introduces new forms of data exposure, misuse of privileges, and adversarial manipulation. The workshop seeks contributions including but not limited secure architectural design, permission frameworks, threat modeling, privacy-preserving methods, and case studies of real-world deployments in order to build a foundation for safer and more trustworthy LLM-empowered super apps and more generally mobile apps.
