Cryptography and Security · Cross-listed from cs.CY
Reliability and Admissibility of AI-Generated Forensic Evidence in Criminal Trials
Sahibpreet Singh and Lalita Devi
This paper examines the admissibility of AI-generated forensic evidence in criminal trials. The growing adoption of AI presents promising results for investigative efficiency. Despite advancements, significant research gaps persist in practically understanding the legal limits of AI evidence in judicial processes. Existing literature lacks focused assessment of the evidentiary value of AI outputs. The objective of this study is to evaluate whether AI-generated evidence satisfies established legal standards of reliability. The methodology involves a comparative doctrinal legal analysis of evidentiary standards across common law jurisdictions. Preliminary results indicate that AI forensic tools can enhance scale of evidence analysis. However, challenges arise from reproducibility deficits. Courts exhibit variability in acceptance of AI evidence due to limited technical literacy and lack of standardized validation protocols. Liability implications reveal that developers and investigators may bear accountability for flawed outputs. This raises critical concerns related to wrongful conviction. The paper emphasizes the necessity of independent validation and, development of AI-specific admissibility criteria. Findings inform policy development for the responsible AI integration within criminal justice systems. The research advances the objectives of Sustainable Development Goal 16 by reinforcing equitable access to justice. Preliminary results contribute for a foundation for future empirical research in AI deployed criminal forensics.
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Cryptography and Security · Cross-listed from cs.DL
IANEC: Digital Forensic Investigation of Contemporary Writers' Archives
Emmanuel Giguet
The IANEC project (Investigation of Digital Archives of Contemporary Writers), led by the GREYC Research Lab and funded by the French Ministry of Culture aims to develop dedicated digital forensic investigation tools to automate the analysis of archival corpora from the Institut Mémoires de l'Édition Contemporaine (IMEC). The project is based on the observation that born-digital archival materials are increasingly prevalent in contemporary archival institutions, and that digital forensics technologies have become essential for the extraction, identification, processing, and description of natively digital archival corpora.*
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Cryptography and Security · Cross-listed from cs.LG
ArcGen: Generalizing Neural Backdoor Detection Across Diverse Architectures
Zhonghao Yang, Cheng Luo, Daojing He et al. (+2 more)
Backdoor attacks pose a significant threat to the security and reliability of deep learning models. To mitigate such attacks, one promising approach is to learn to extract features from the target model and use these features for
backdoor detection. However, we discover that existing learning-based neural
backdoor detection methods do not generalize well to new architectures not seen during the learning phase. In this paper, we analyze the root cause of this issue and propose a novel black-box neural
backdoor detection method called ArcGen. Our method aims to obtain architecture-invariant model features, i.e., aligned features, for effective
backdoor detection. Specifically, in contrast to existing methods directly using model outputs as model features, we introduce an additional
alignment layer in the feature extraction function to further process these features. This reduces the direct influence of architecture information on the features. Then, we design two
alignment losses to train the feature extraction function. These losses explicitly require that features from models with similar
backdoor behaviors but different architectures are aligned at both the distribution and sample levels. With these techniques, our method demonstrates up to 42.5% improvements in detection performance (e.g., AUC) on unseen model architectures. This is based on a large-scale evaluation involving 16,896 models trained on diverse
datasets, subjected to various
backdoor attacks, and utilizing different model architectures. Our code is available at https://github.com/SeRAlab/ArcGen.
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All authors Zhonghao Yang, Cheng Luo, Daojing He, Yiming Li, Yu Li
Cryptography and Security · Cross-listed from quant-ph
Random coding for long-range continuous-variable QKD
Arpan Akash Ray and Boris Skoric
Quantum Key Distribution (QKD) schemes are key exchange protocols based on the physical properties of
quantum channels. They avoid the computational-hardness assumptions that underlie the security of classical key exchange. Continuous-Variable QKD (CVQKD), in contrast to
qubit-based discrete-variable (DV) schemes, makes use of quadrature measurements of the electromagnetic field. CVQKD has the advantage of being compatible with standard telecom equipment, but at long distances has to deal with very low signal to noise ratios, which necessitates labour-intensive error correction. It is challenging to implement the error correction decoding in realtime. In this paper we introduce a random-codebook error correction method that is suitable for long range Gaussian-modulated CVQKD. We use likelihood ratio scoring with block rejection based on thresholding. For proof-technical reasons, the accept/reject decisions are communicated in encrypted form; in this way we avoid having to deal with non-Gaussian states in the analysis of the leakage. The error correction method is highly parallelisable, which is advantageous for realtime implementation. Under conservative assumptions on the computational resources, we predict a realtime key ratio of at least 8% of the Devetak-Winter value, which outperforms existing reconciliation schemes.
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Cryptography and Security
Charge It to My Neighbor: A Relay Attack on ISO 15118 Plug and Charge Payment
Jakob Löw, Vishwa Vasu, Thomas Hutzelmann et al. (+1 more)
ISO 15118, the leading standard for DC fast charging in Europe, includes a plug-and-charge mechanism that allows electric vehicles to handle payment automatically via contract certificates. We present a novel relay attack against this mechanism: an attacker builds a fake charging station, plugs it into a victim's vehicle, and relays the cryptographic authentication to a real charging station - charging the attacker's vehicle while billing the victim. The attack exploits the absence of station-identifying information in the plug-and-charge signature, combined with weaknesses in how ISO 15118 handles TLS certificates. We provide a proof-of-concept implementation demonstrating the full attack chain and discuss possible mitigations and alternatives. As plug-and-charge adoption grows, addressing this vulnerability is critical before it becomes widely exploitable.
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All authors Jakob Löw, Vishwa Vasu, Thomas Hutzelmann, Hans-Joachim Hof
Cryptography and Security · Cross-listed from cs.CY
Analysing Multidisciplinary Approaches to Fight Large-Scale Digital Influence Operations
David Arroyo, Rafael Mata Milla, Marc Almeida Ros et al. (+4 more)
Crime as a Service (CaaS) has evolved from isolated criminal incidents to a broad spectrum of illicit activities, including social media manipulation, foreign information manipulation and interference (FIMI), and the sale of disinformation toolkits. This article analyses how threat actors exploit specialised infrastructures ranging from proxy and VPN services to AI-driven generative models to orchestrate large-scale opinion manipulation. Moreover, it discusses how these malicious operations monetise the virality of social networks, weaponise dual-use technologies, and leverage user biases to amplify polarising narratives. In parallel, it examines key strategies for detecting, attributing, and mitigating such campaigns by highlighting the roles of blockchain-based content verification, advanced cryptographic proofs, and cross-disciplinary collaboration. Finally, the article highlights that countering disinformation demands an integrated framework that combines legal, technological, and societal efforts to address a rapidly adapting and borderless threat
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All authors David Arroyo, Rafael Mata Milla, Marc Almeida Ros, Nikolaos Lykousas, Ivan Homoliak, Constantinos Patsakis, Fran Casino
Cryptography and Security
Private Virtual Tree Networks for Secure Multi-Tenant Environments Based on the VIRGO Overlay Network
Lican Huang
Hierarchical organization is a fundamental structure in real-world society, where authority and responsibility are delegated from managers to subordinates. The VIRGO network (Virtual Hierarchical Overlay Network for scalable grid computing) provides a scalable overlay for organizing distributed systems but lacks intrinsic security and
privacy mechanisms. This paper proposes Private Virtual Tree Networks (PVTNs), a cryptographically enforced extension that leverages the VIRGO overlay to mirror real organizational hierarchies. In PVTNs, join requests are encrypted with the manager's public key to ensure confidentiality, while membership authorization is enforced through manager-signed delegation certificates. Public keys are treated as organizational secrets and are disclosed only within direct manager-member relationships, resulting in a private, non-enumerable virtual tree. Our work demonstrates, through the system model, protocols, security analysis, and design rationale, that PVTNs achieve scalability, dynamic management, and strong security guarantees without relying on global public key infrastructures.
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Cryptography and Security
VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Traces
Artem Grigor, Christian Schroeder de Witt, Simon Birnbach et al. (+1 more)
Recent advances in large
language models (LLMs) have enabled a new generation of autonomous
agents that operate over sustained periods and manage sensitive resources on behalf of users. Trusted for their ability to act without direct oversight, such
agents are increasingly considered in high-stakes domains including financial management, dispute resolution, and governance. Yet in practice,
agents execute on infrastructure controlled by a host, who can tamper with models, inputs, or outputs, undermining any meaningful notion of autonomy. We address this gap by introducing VET (Verifiable Execution Traces), a formal framework that achieves host-independent authentication of
agent outputs and takes a step toward host-independent autonomy. Central to VET is the
Agent Identity Document (AID), which specifies an
agent's configuration together with the proof systems required for verification. VET is compositional: it supports multiple proof mechanisms, including trusted hardware, succinct cryptographic proofs, and notarized TLS transcripts (Web Proofs). We implement VET for an API-based LLM
agent and evaluate our instantiation on realistic workloads. We find that for today's black-box, secret-bearing API calls, Web Proofs appear to be the most practical choice, with overhead typically under 3$\times$ compared to direct API calls, while for public API calls, a lower-overhead TEE Proxy is often sufficient. As a case study, we deploy a verifiable trading
agent that produces proofs for each decision and composes Web Proofs with a TEE Proxy. Our results demonstrate that practical, host-agnostic authentication is already possible with current technology, laying the foundation for future systems that achieve full host-independent autonomy.
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All authors Artem Grigor, Christian Schroeder de Witt, Simon Birnbach, Ivan Martinovic
Cryptography and Security
Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications
Mohammad Waquas Usmani, Sankalpa Timilsina, Michael Zink et al. (+1 more)
Immersive formats such as 360{\deg} and 6DoF point cloud
videos require high bandwidth and low latency, posing challenges for real-time AR/VR streaming. This work focuses on reducing bandwidth consumption and encryption/decryption delay, two key contributors to overall latency. We design a system that downsamples point cloud content at the origin server and applies partial encryption. At the client, the content is decrypted and upscaled using an ML-based super-resolution model. Our evaluation demonstrates a nearly linear reduction in bandwidth/latency, and encryption/decryption overhead with lower downsampling resolutions, while the super-resolution model effectively reconstructs the original full-resolution point clouds with minimal error and modest inference time.
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All authors Mohammad Waquas Usmani, Sankalpa Timilsina, Michael Zink, Susmit Shannigrahi
Cryptography and Security
Unveiling the Attribute Misbinding Threat in Identity-Preserving Models
Junming Fu, Jishen Zeng, Yi Jiang et al. (+4 more)
Identity-preserving models have led to notable progress in generating personalized content. Unfortunately, such models also exacerbate risks when misused, for instance, by generating threatening content targeting specific individuals. This paper introduces the Attribute Misbinding Attack, a novel method that poses a threat to identity-preserving models by inducing them to produce Not-Safe-For-Work (NSFW) content. The attack's core idea involves crafting benign-looking textual
prompts to circumvent text-filter safeguards and leverage a key model vulnerability: flawed attribute binding that stems from its internal
attention bias. This results in misattributing harmful descriptions to a target identity and generating NSFW outputs. To facilitate the study of this attack, we present the Misbinding
Prompt evaluation set, which examines the content generation risks of current state-of-the-art identity-preserving models across four risk dimensions: pornography, violence, discrimination, and illegality. Additionally, we introduce the Attribute Binding Safety Score (ABSS), a metric for concurrently assessing both content fidelity and safety compliance. Experimental results show that our Misbinding
Prompt evaluation set achieves a 5.28% higher success rate in bypassing five leading text filters (including GPT-4o) compared to existing main-stream evaluation sets, while also demonstrating the highest proportion of NSFW content generation. The proposed ABSS metric enables a more comprehensive evaluation of identity-preserving models by concurrently assessing both content fidelity and safety compliance.
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All authors Junming Fu, Jishen Zeng, Yi Jiang, Peiyu Zhuang, Baoying Chen, Siyu Lu, Jianquan Yang
Cryptography and Security · Cross-listed from cs.DB
Implementing a Scalable, Redeployable and Multitiered Repository for FAIR and Secure Scientific Data Sharing: The BIG-MAP Archive
Valeria Granata, Francois Liot, Xing Wang et al. (+5 more)
Data sharing in large consortia, such as research collaborations or industry partnerships, requires addressing both organizational and technical challenges. A common platform is essential to promote collaboration, facilitate exchange of findings, and ensure secure access to sensitive data. Key technical challenges include creating a scalable architecture, a user-friendly interface, and robust security and access control. The BIG-MAP Archive is a cloud-based, disciplinary, private repository designed to address these challenges. Built on InvenioRDM, it leverages platform functionalities to meet consortium-specific needs, providing a tailored solution compared to general repositories. Access can be restricted to members of specific communities or open to the entire consortium, such as the BATTERY 2030+, a consortium accelerating advanced battery technologies. Uploaded data and metadata are controlled via fine grained permissions, allowing access to individual project members or the full initiative. The formalized upload process ensures data are formatted and ready for publication in open repositories when needed. This paper reviews the repository's key features, showing how the BIG-MAP Archive enables secure, controlled data sharing within large consortia. It ensures data confidentiality while supporting flexible, permissions-based access and can be easily redeployed for other consortia, including MaterialsCommons4.eu and RAISE (Resource for AI Science in Europe).
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All authors Valeria Granata, Francois Liot, Xing Wang, Steen Lysgaard, Ivano E. Castelli, Tejs Vegge, Nicola Marzari, Giovanni Pizzi
Cryptography and Security
BashArena: A Control Setting for Highly Privileged AI Agents
Adam Kaufman, James Lucassen, Tyler Tracy et al. (+2 more)
Future AI
agents might run autonomously with elevated privileges. If these
agents are misaligned, they might abuse these privileges to cause serious damage. The field of AI control develops techniques that make it harder for misaligned AIs to cause such damage, while preserving their usefulness. We introduce BashArena, a setting for studying AI control techniques in security-critical environments. BashArena contains 637 Linux system administration and infrastructure engineering tasks in complex, realistic environments, along with four sabotage objectives (execute malware, exfiltrate secrets, escalate privileges, and disable firewall) for a red team to target. We evaluate multiple frontier LLMs on their ability to complete tasks, perform sabotage undetected, and detect sabotage attempts. Claude Sonnet 4.5 successfully executes sabotage while evading monitoring by GPT-4.1 mini 26% of the time, at 4% trajectory-wise FPR. Our findings provide a baseline for designing more effective control protocols in BashArena. We release the
dataset as a ControlArena setting and share our task generation pipeline.
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All authors Adam Kaufman, James Lucassen, Tyler Tracy, Cody Rushing, Aryan Bhatt
Cryptography and Security
Distributed HDMM: Scalable, Distributed, Accurate, and Differentially Private Query Workloads without a Trusted Curator
Ratang Sedimo, Ivoline C. Ngong, Jami Lashua et al. (+1 more)
We present the Distributed High-Dimensional
Matrix Mechanism (Distributed HDMM), a protocol for answering workloads of linear queries on distributed data that provides the accuracy of central-model HDMM without a trusted curator. Distributed HDMM leverages a secure aggregation protocol to evaluate HDMM on distributed data, and is secure in the context of a malicious aggregator and malicious clients (assuming an honest majority). Our preliminary empirical evaluation shows that Distributed HDMM can run on realistic
datasets and workloads with thousands of clients in less than one minute.
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All authors Ratang Sedimo, Ivoline C. Ngong, Jami Lashua, Joseph P. Near
Cryptography and Security · Cross-listed from cs.AR
FAME: FPGA Acceleration of Secure Matrix Multiplication with Homomorphic Encryption
Zhihan Xu, Rajgopal Kannan and Viktor K. Prasanna
Homomorphic Encryption (HE) enables secure computation on encrypted data, addressing
privacy concerns in cloud computing. However, the high computational cost of HE operations, particularly
matrix multiplication (MM), remains a major barrier to its practical deployment. Accelerating homomorphic encrypted MM (HE MM) is therefore crucial for applications such as
privacy-preserving machine learning. In this paper, we present a bandwidth-efficient FPGA implementation of HE MM. We first develop a cost model to evaluate the on-chip
memory requirements for a given set of HE parameters and input
matrix sizes. Our analysis shows that optimizing on-chip
memory usage is critical for scalable and efficient HE MM. To this end, we design a novel datapath for Homomorphic Linear Transformation (HLT), the primary bottleneck in HE MM. The proposed datapath significantly reduces off-chip
memory traffic and on-chip
memory demand by enabling fine-grained data reuse. Leveraging this datapath, we introduce FAME, the first FPGA-based accelerator specifically tailored for HE MM. FAME supports arbitrary
matrix shapes and is configurable across a wide range of HE parameter sets. We implement FAME on an Alveo U280 FPGA and evaluate its performance across diverse
matrix sizes and shapes. Experimental results show that FAME achieves an average speedup of 221x over state-of-the-art CPU-based implementations, demonstrating its scalability and practicality for large-scale consecutive HE MM and real-world workloads.
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Cryptography and Security
Time will Tell: Large-scale De-anonymization of Hidden I2P Services via Live Behavior Alignment (Extended Version)
Hongze Wang, Zhen Ling, Xiangyu Xu et al. (+4 more)
I2P (Invisible Internet Project) is a popular anonymous communication network. While existing de-anonymization methods for I2P focus on identifying potential traffic patterns of target hidden services among extensive network traffic, they often fail to scale effectively across the large and diverse I2P network, which consists of numerous routers. In this paper, we introduce I2PERCEPTION a low-cost approach revealing the IP addresses of I2P hidden services. In I2PERCEPTION, attackers deploy floodfill routers to passively monitor I2P routers and collect their RouterInfo. We analyze the router information publication mechanism to accurately identify routers' join (i.e. on) and leave (i.e. off) behaviors, enabling fine-grained live behavior inference across the I2P network. Active probing is used to obtain the live behavior (i.e., on-off patterns) of a target hidden service hosted on one of the I2P routers. By correlating the live behaviors of the target hidden service and I2P routers over time, we narrow down the set of routers matching the hidden service's behavior, revealing the hidden service's true network identity for de-anonymization. Through the deployment of only 15 floodfill routers over the course of eight months, we validate the precision and effectiveness of our approach with extensive real-world experiments. Our results show that I2PERCEPTION successfully de-anonymizes all controlled hidden services.
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All authors Hongze Wang, Zhen Ling, Xiangyu Xu, Yumingzhi Pan, Guangchi Liu, Junzhou Luo, Xinwen Fu
Cryptography and Security
Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection
Konstantinos Kalogiannis, Ahmed Mohamed Hussain, Hexu Li et al. (+1 more)
Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations enabled by Vehicle-to-Everything (V2X) communication. However, the distributed nature of platoon coordination creates security vulnerabilities, allowing authenticated vehicles to inject falsified kinematic data, compromise operational stability, and pose a threat to passenger safety. Traditional misbehaviour detection approaches, which rely on plausibility checks and statistical methods, suffer from high False Positive (FP) rates and cannot capture the complex temporal dependencies inherent in multi-vehicle coordination dynamics. We present
Attention In Motion (AIMformer), a
transformer-based framework specifically tailored for real-time misbehaviour detection in vehicular platoons with edge deployment capabilities. AIMformer leverages multi-head self-
attention mechanisms to capture intra-vehicle temporal dynamics, with a spatio-temporal variant that further models inter-vehicle spatial correlations. It incorporates global positional encoding with vehicle-specific temporal offsets to handle join/exit maneuvers. We propose a Precision-Focused Binary Cross-
Entropy (PFBCE) loss function that penalizes FPs to meet the requirements of safety-critical vehicular systems. Extensive evaluation across 4 platoon controllers, multiple attack vectors, and diverse mobility scenarios demonstrates superior performance ($\geq$ 0.93) compared to state-of-the-art baseline architectures. A comprehensive deployment analysis utilizing TensorFlow Lite (TFLite), Open Neural Network Exchange (ONNX), and TensorRT achieves sub-millisecond inference latency, making it suitable for real-time operation on resource-constrained edge platforms. Hence, validating AIMformer is viable for both in-vehicle and roadside deployment.
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All authors Konstantinos Kalogiannis, Ahmed Mohamed Hussain, Hexu Li, Panos Papadimitratos
Cryptography and Security · Cross-listed from cs.SE
How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?
Hua Yang, Alejandro Velasco, Thanh Le-Cong et al. (+3 more)
The success of large
language models for code relies on vast amounts of code data, including public open-source repositories, such as GitHub, and private, confidential code from companies. This raises concerns about intellectual property compliance and the potential unauthorized use of license-restricted code. While membership inference (MI) techniques have been proposed to detect such unauthorized usage, their effectiveness can be undermined by semantically equivalent code transformation techniques, which modify code syntax while preserving semantic. In this work, we systematically investigate whether semantically equivalent code transformation rules might be leveraged to evade MI detection. The results reveal that model accuracy drops by only 1.5% in the worst case for each rule, demonstrating that transformed
datasets can effectively serve as substitutes for fine-tuning. Additionally, we find that one of the rules (RenameVariable) reduces MI success by 10.19%, highlighting its potential to obscure the presence of restricted code. To validate these findings, we conduct a causal analysis confirming that variable renaming has the strongest causal effect in disrupting MI detection. Notably, we find that combining multiple transformations does not further reduce MI effectiveness. Our results expose a critical loophole in license compliance enforcement for training large
language models for code, showing that MI detection can be substantially weakened by transformation-based obfuscation techniques.
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All authors Hua Yang, Alejandro Velasco, Thanh Le-Cong, Md Nazmul Haque, Bowen Xu, Denys Poshyvanyk
Cryptography and Security · Cross-listed from cs.SE
Insecure Ingredients? Exploring Dependency Update Patterns of Bundled JavaScript Packages on the Web
Ben Swierzy, Marc Ohm and Michael Meier
Reusable software components, typically distributed as packages, are a central paradigm of modern software development. The JavaScript ecosystem serves as a
prime example, offering millions of packages with their use being promoted as idiomatic. However, download statistics on npm raise security concerns as they indicate a high popularity of vulnerable package versions while their real prevalence on production websites remains unknown. Package version detection mechanisms fill this gap by extracting utilized packages and versions from observed artifacts on the web. Prior research focuses on mechanisms for either hand-selected popular packages in bundles or for single-file resources utilizing the global namespace. This does not allow for a thorough analysis of modern web applications' dependency update behavior at scale. In this work, we improve upon this by presenting Aletheia, a package-agnostic method which dissects JavaScript bundles to identify package versions through
algorithms originating from the field of plagiarism detection. We show that this method clearly outperforms the existing approaches in practical settings. Furthermore, we crawl the Tranco top 100,000 domains to reveal that 5% - 20% of domains update their dependencies within 16 weeks. Surprisingly, from a longitudinal perspective, bundled packages are updated significantly faster than their CDN-included counterparts, with consequently up to 10 times fewer known vulnerable package versions included. Still, we observe indicators that few widespread vendors seem to be a major driving force behind timely updates, implying that quantitative measures are not painting a complete picture.
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Cryptography and Security
Packed Malware Detection Using Grayscale Binary-to-Image Representations
Ehab Alkhateeb, Ali Ghorbani and Arash Habibi Lashkari
Detecting packed executables is a critical step in malware analysis, as packing obscures the original code and complicates static inspection. This study evaluates both classical feature-based methods and deep learning approaches that transform binary executables into visual representations, specifically, grayscale byte plots, and employ convolutional neural networks (CNNs) for automated classification of packed and non-packed binaries. A diverse
dataset of benign and malicious Portable Executable (PE) files, packed using various commercial and open-source packers, was curated to capture a broad spectrum of packing transformations and obfuscation techniques. Classical models using handcrafted Gabor jet features achieved intense discrimination at moderate computational cost. In contrast, CNNs based on VGG16 and DenseNet121 significantly outperformed them, achieving high detection performance with well-balanced precision, recall, and F1-scores. DenseNet121 demonstrated slightly higher precision and lower false positive rates, whereas VGG16 achieved marginally higher recall, indicating complementary strengths for practical deployment. Evaluation against unknown packers confirmed robust generalization, demonstrating that grayscale byte-plot representations combined with deep learning provide a useful and reliable approach for early detection of packed malware, enhancing malware analysis pipelines and supporting automated antivirus inspection.
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Cryptography and Security
Talking to the Airgap: Exploiting Radio-Less Embedded Devices as Radio Receivers
Paul Staat, Daniel Davidovich and Christof Paar
Physical isolation from external networks - an airgap - aims to minimize exposure to remote attacks. Yet capable adversaries still achieve code execution on air-gapped systems, and prior work has shown that they can then wirelessly exfiltrate data via unintended emissions. In this work, we demonstrate the reverse direction: malicious code on an embedded device enables wireless infiltration of air-gapped systems, granting attackers command-and-control over compromised targets. Leveraging physical effects previously studied in the context of electromagnetic interference (EMI), we show that parasitic
radio frequency (RF) sensitivity in printed
circuit board (PCB) traces and on-chip analog-to-digital converters (ADCs) turns commodity embedded devices into inadvertent
radio receivers. Unlike prior infiltration techniques, our approach requires no dedicated sensors (e.g., microphones, LEDs, or temperature sensors) and works in non-line-of-sight scenarios. In our evaluation, an ordinary microcontroller evaluation board reliably recovers communication signals from tens of meters at data rates of up to 100 kbps. Applying a systematic methodology to discover such device-intrinsic RF sensitivity, we evaluate twelve commercial embedded devices and two custom prototypes, finding that all exhibit reception capabilities in the 300-1000 MHz range. Our findings challenge the assumption that embedded devices without
radios lack an inbound
radio paths and call for air-gap threat models that account for both emission-based leakage and unintended reception.
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Cryptography and Security · Cross-listed from cs.RO
Remotely Detectable Robot Policy Watermarking
Michael Amir, Manon Flageat and Amanda Prorok
The success of machine learning for real-world robotic systems has created a new form of intellectual property: the trained policy. This raises a critical need for novel methods that verify ownership and detect unauthorized, possibly unsafe misuse. While watermarking is established in other domains, physical policies present a unique challenge: remote detection. Existing methods assume access to the
robot's internal state, but auditors are often limited to external observations (e.g.,
video footage). This “Physical Observation Gap” means the watermark must be detected from signals that are noisy, asynchronous, and filtered by unknown system dynamics. We formalize this challenge using the concept of a glimpse sequence, and introduce Colored Noise Coherency (CoNoCo), the first watermarking strategy designed for remote detection. CoNoCo embeds a spectral signal into the
robot's motions by leveraging the policy's inherent stochasticity. To show it does not degrade performance, we prove CoNoCo preserves the marginal action distribution. Our experiments demonstrate strong, robust detection across various remote modalities, including motion capture and side-way/top-down
video footage, in both simulated and real-world
robot experiments. This work provides a necessary step toward protecting intellectual property in robotics, offering the first method for validating the provenance of physical policies non-invasively, using purely remote observations.
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Cryptography and Security · Cross-listed from cs.LG
Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
Chenxiang Zhang, Tongxi Qu, Zhong Li et al. (+3 more)
Deep neural networks are widely deployed with quantization techniques to reduce
memory and computational costs by lowering the numerical precision of their parameters. While quantization alters model parameters and their outputs, existing
privacy analyses primarily focus on full-precision models, leaving a gap in understanding how bit-width reduction can affect
privacy leakage. We present the first systematic study of the
privacy-utility relationship in post-training quantization (PTQ), a versatile family of methods that can be applied to pretrained models without further training. Using membership inference attacks as our evaluation framework, we analyze three popular PTQ
algorithms-AdaRound, BRECQ, and OBC-across multiple precision levels (4-bit, 2-bit, and 1.58-bit) on CIFAR-10, CIFAR-100, and TinyImageNet
datasets. Our findings consistently show that low-precision PTQs can reduce
privacy leakage. In particular, lower-precision models demonstrate up to an order of magnitude reduction in membership inference vulnerability compared to their full-precision counterparts, albeit at the cost of decreased utility. Additional ablation studies on the 1.58-bit quantization level show that quantizing only the last layer at higher precision enables fine-grained control over the
privacy-utility trade-off. These results offer actionable insights for practitioners to balance efficiency, utility, and
privacy protection in real-world deployments.
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All authors Chenxiang Zhang, Tongxi Qu, Zhong Li, Tian Zhang, Jun Pang, Sjouke Mauw
Cryptography and Security · Cross-listed from quant-ph
Practical Challenges in Executing Shor's Algorithm on Existing Quantum Platforms
Paul Bagourd, Julian Jang-Jaccard, Vincent Lenders et al. (+3 more)
Quantum computers pose a fundamental threat to widely deployed public-key cryptosystems, such as RSA and ECC, by enabling efficient integer factorization using Shor's
algorithm. Theoretical resource estimates suggest that 2048-bit RSA keys could be broken using Shor's
algorithm with fewer than a million noisy
qubits. Although such machines do not yet exist, the availability of smaller, cloud-accessible
quantum processors and open-source implementations of Shor's
algorithm raises the question of what key sizes can realistically be factored with today's platforms. In this work, we experimentally investigate Shor's
algorithm on several cloud-based
quantum computers using publicly available implementations. Our results reveal a substantial gap between the capabilities of current
quantum hardware and the requirements for factoring cryptographically relevant integers. In particular, we observe that
circuit constructions still need to be highly specific for each modulus, and that machine fidelities are unstable, with high and fluctuating error rates.
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All authors Paul Bagourd, Julian Jang-Jaccard, Vincent Lenders, Alain Mermoud, Torsten Hoefler, Cornelius Hempel
Cryptography and Security · Cross-listed from cs.LG
Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
Siva Sai, Ishika Goyal, Shubham Sharma et al. (+3 more)
The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rendering them unable to keep up. An alternative,
Quantum Machine Learning (QML), has recently emerged, making use of computations based on
quantum mechanics. It offers better encoding and processing of high-dimensional structures for certain problems. This survey provides a comprehensive overview of QML techniques relevant to the domain of security, such as
Quantum Neural Networks (QNNs),
Quantum Support Vector Machines (QSVMs), Variational
Quantum Circuits (VQCs), and
Quantum Generative
Adversarial Networks (QGANs), and discusses the contributions of this paper in relation to existing research in the field and how it improves over them. It also maps these methods across supervised, unsupervised, and generative learning paradigms, and to core cybersecurity tasks, including intrusion and anomaly detection, malware and botnet classification, and encrypted-traffic analytics. It also discusses their application in the domain of cloud computing security, where QML can enhance secure and scalable operations. Many limitations of QML in the domain of cybersecurity have also been discussed, along with the directions for addressing them.
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All authors Siva Sai, Ishika Goyal, Shubham Sharma, Sri Harshita Manuri, Vinay Chamola, Rajkumar Buyya