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arXiv2026

eBPF-Based Cybersecurity Mechanisms: A Systematic Literature Review

Extended Berkeley Packet Filter (eBPF) has emerged as a kernel-level framework enabling dynamic security enforcement in modern operating systems. While eBPF's cybersecurity potential has attracted significant attention, existing work remains fragmented across domains, evaluation methodologies, and deployment contexts. This systematic literature review applies PRISMA methodology to identify, categorize, and synthesize peer-reviewed research on eBPF-based cybersecurity mechanisms. Following a structured screening of 3,735 records from six databases, 54 primary studies (2018-2026) were analyzed and organized into a seven-domain taxonomy: DDoS mitigation, intrusion detection, IoT security, container security, microservice protection, networking, and security tools. Analysis reveals eBPF enables low-overhead security enforcement (median 2.4% CPU overhead [1.1-8.6%], ranging from nanosecond-scale costs for infrequent hooks to 10-20% for kernel hot paths) with high detection accuracy (94-99%). It particularly excels in kernel-level monitoring, real-time packet processing, and cloud-native workload protection. However, significant challenges persist: verifier-imposed constraints limit algorithm complexity, 85.1% (46/54) of studies require low-level programming expertise, kernel version fragmentation hinders portability, and 96.2% (52/54) of research fails to address eBPF's own vulnerabilities. This review identifies critical research gaps in multi-tenant isolation, adversarial machine learning (ML) robustness, production validation, and standardized evaluation frameworks. By consolidating fragmented knowledge and highlighting architectural trade-offs between safety and expressiveness, this work provides a foundation for next-generation eBPF security systems and actionable directions for kernel programmability research.

cs.CR↗
arXiv2026

Can Tainted Pixels Expose Deepfake Videos?

Publicly-acceesible face-manipulation tools have made deepfake creation accessible to non-expert users. Against these, existing defenses are mostly post-hoc, detecting only after forgery has occurred, and operating on still images rather than videos. Research is lacking in i) the proactive protection of published facial videos against black-box manipulation tools, and in (ii) understanding its perceptual effect on human viewers. We introduce TaintedPixels, a proactive video-protection method built around an asymmetric visibility trade-off: the embedded watermark should remain inconspicuous in the published video but become obvious once a downstream tool manipulates the video. TaintedPixels injects structured periodic perturbations into the blue channel of facial regions and refines them under stripe-visibility, color-cast, and video-level LPIPS budgets, with lightweight motion-adaptive deployment. We believe TaintedPixels is the first proactive defense designed specifically against black-box manipulation tools rather than image-level pipelines or specific surrogate generators. Across three publicly available off-the-shelf video manipulation tools and two off-the-shelf detectors, TaintedPixels attains the highest forgery fake rate while keeping perturbations small (LPIPS = 0.0042). Our non-expert human study, conducted on a diverse set of 300 video stimuli spanning different lighting conditions, backgrounds, and skin tones, shows that protected source videos draw a 3.26% suspicion rate, while forgeries from protected sources are identified as fake much more often than forgeries from unprotected sources (90.72% vs. 56.71%). This validates the effectiveness of TaintedPixels.

cs.CR↗
arXiv2026

What's Your NIC Whispering? Network Threat Behavior Recognition via NIC Electromagnetic Side-Channel Leakage

Conventional network threat detection primarily relies on packet-level, flow-level, or host-level telemetry. This paper investigates a different observation surface: unintended electromagnetic(EM) emissions generated by network interface card(NIC) activity, and asks whether such physical leakage contains sufficiently structured information for network threat-behavior recognition. We present NICWhisper, which externally captures NIC EM emissions, transforms raw measurements into time-frequency representations, and recognizes network behaviors without inspecting packet contents or host-side runtime states. Rather than competing with traffic-based detection, NICWhisper exploits the physical manifestation of traffic-driven NIC activity, whose timing, rate, concurrency, and burst organization naturally shape the measured EM leakage. We construct a NIC EM dataset covering active benign workloads and seven representative threat behaviors under diverse execution conditions, and systematically evaluate signal dependence, execution variation, measurement perturbation, and cross-device transfer. NICWhisper achieves 80.67\% Macro-F1 across eight behavior classes, while further experiments show that the observed behavior-related information extends beyond simple signal magnitude and remains partially transferable across execution conditions and NIC hardware. These results establish NIC EM leakage as a complementary physical observation source for network security monitoring when direct access to conventional traffic or host telemetry is limited or undesirable.

cs.CR↗
arXiv2026

Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation

The detection of rare disease-associated cell subsets from high-dimensional single-cell measurements is critical for understanding diseases such as leukaemia and viral infections. CellCnn, a convolutional neural network (CNN) designed for this task, has demonstrated the ability to identify phenotype-associated cell populations at frequencies as low as 0.01\%. Training such models reliably requires patient cohorts that are larger and more diverse than any single institution can typically assemble, and the underlying single-cell data is too sensitive to share across institutional boundaries under existing privacy regulations. We propose a secure multi-party computation (MPC) framework that enables the training and inference of CellCnn entirely on secret-shared data. This ensures that neither the participants nor the computing servers ever observe raw patient data or intermediate values. Evaluated on benchmark single-cell datasets for cytomegalovirus infection (CMV) and acute myeloid leukaemia (AML), our implementation preserves accuracy close to its plaintext counterpart while outperforming the prior privacy-preserving baseline. In contrast to earlier privacy-preserving approaches that removed components such as ReLU activations and bias terms, our method retains these key parts of the CellCnn architecture and supports accurate analysis without exposing raw patient data.

cs.CR↗
arXiv2026

Rotational Bell-State Loop-Back Key Establishment and Agreement Certification for Passive Users

Server-aided Bell-state networks with sequential local encoding are an established approach to mediated key distribution. Against this background, we study a Loop-Back architecture in which two source-free, detector-free users transform the same traveling qubit. Alice privately samples $r\in\mathbb{Z}*2^2$, prepares $|β_r\rangle$ over the complete Bell basis, retains one qubit, and uses $r$ as the reference for the returned pair. A deterministic Pauli-composition mode, $U*{\mathrm{eff}}=U_2U_1$, serves as a reference to prior serial-unitary protocols. The main extension instead uses the two same-axis settings $R(\pmα)$ and a reference-versus-complement Bell test. Opposite rotations cancel, whereas equal rotations add, so $P(C\mid\mathrm{disagreement})=0$ and $P(C\mid\mathrm{agreement})=\sin^2(2α)$. At $α=π/8$, agreement is certified with probability $1/2$, and the overall conclusive-key probability is $1/4$ for unbiased choices. The same event supplies one shared raw-key bit. For the Pauli mode, a one-qubit Pauli twirl hides either user's factor from an endpoint mediator that may prepare an arbitrary qubit--ancilla state and choose the final measurement, provided the other Pauli is uniform and the traveler is inaccessible between users. The rotational guarantee is narrower: common-sign privacy holds only for the prescribed Bell source and coarse-grained instrument. A polarization implementation can normalize the private reference to the singlet and use a beam splitter with verified two-photon detection; loss produces a third, discarded outcome. The proposal is theoretical and does not claim composable security or experimental validation.

quant-ph↗
arXiv2026

VR-Themis: A Scalable Framework for Virtual Reality Application Clone Detection

Repackaging of mobile applications (aka app cloning) not only threatens the security and privacy of mobile users but also infringes upon the copyright of the original app developers. However, existing detection methods that primarily focus on mobile platforms (such as Android) fail to capture the essential features of virtual reality (VR). Consequently, they are inadequate for effectively detecting cloned VR apps, which have often been targeted by illegal users in the VR market. Considering the unique features of VR apps, this paper proposes a two-stage app clone detection framework, namely VR-Themis, based on \emph{Hierarchy-Object-Behaviour} (HOB). Firstly, VR-Themis exploits the coarse-grained stage to cluster apps based on their retrievable statistical features, making this tool scalable to large-scale VR app datasets. Then, in the fine-grained stage, VR-Themis performs in-depth analysis of the suspicious apps (identified in the first stage) by calculating similarity using our defined \emph{HOB metrics}. Our extensive experiments indicate that VR-Themis successfully detects 307 suspected clone apps from the collected 4,277 VR apps without false positives, demonstrating its effectiveness and scalability.

cs.CR↗
arXiv2026

Long-Horizon State Tracking in LLMs: Executing MD5 through a Deep Sequence of Dependent Tool Calls

Long-horizon tasks remain uncommon in large language model (LLM) evaluation, and for a reason: when each step depends on the last, per-step accuracy that looks excellent in isolation decays catastrophically, as errors cascade and the end-to-end failure probability grows sharply with length. Existing agentic benchmarks report end-to-end success but confound this state-tracking difficulty with instruction interpretation, give no control group that isolates it, and are vulnerable to shortcuts such as a hallucinated final answer, so they cannot say why a long run fails. Whether an LLM can carry exact intermediate state across many tool calls at all is itself not well established. We test this cleanly by having the model compute a cryptographic hash, MD5, step by step: a sequence of $196$ dependent tool calls over $64$ rounds while it carries four $32$-bit words $(a,b,c,d)$ in its own context from one call to the next. Interpretation is trivial and, because we implement MD5 from scratch (RFC~1321), we align every call to the ground-truth trace and check the digest to the bit, so any failure is pure bookkeeping. gpt-oss-120b, a mixture-of-experts model with only $\sim$5.5B active parameters per token, at temperature $0$ with a short fixed prompt, carries the full state across all $196$ calls and returns the correct digest on a majority of completed runs. In the strongest setting we replace every primitive tool with a second LLM, so a driver and a worker compute the whole hash from scratch with no exact-arithmetic oracle in the loop. Two ingredients decide success and neither changes the weights: keeping the model's own reasoning in its context each turn, and voting over a thinking-enabled worker to remove its modular-arithmetic slips. We localize the residual failures by origin, separating state-carrying from arithmetic and from serving.

cs.AI↗
arXiv2026

Federated Trust for Embodied Robot Capability Marketplaces

Robot capability marketplaces, the "app store for robot skills," are emerging as the deployment vector for LLM-driven robot fleets. The default cloud-native answer to "is this package safe to install?" is centralised PKI: one certificate authority, one transparency log, one root of trust. We argue this is the wrong model for embodied robot fleets, where operators face heterogeneous regulatory regimes, air-gapped deployments, tiny operator headcounts, and physical-world consequences for trusting the wrong publisher. We present federated trust: each deployed bridge maintains its own local trust directory of acceptable signers; signers identify themselves with a public key embedded in a detached Ed25519 signature envelope; install-time verification is a local set-membership check rather than a network round trip to a certificate authority. The cryptographic primitives are deliberately standard (Ed25519 detached signatures and SSH-style trust files); the contribution is the architectural commitment that this composition fits embodied robot fleets specifically. We implement the model in a runtime governance layer with a five-subcommand CLI, a registry server, a per-bridge install gate, and 80 tests. A multi-deployment evaluation shows the same registry stream producing divergent install verdicts on bridges with different trust directories, the load-bearing design property. Across 5000 adversarial trials, the strict-mode gate rejects 100% of rogue-publisher, tampered, forged, and revoked-signer attacks, and 96.6% of downgrade attempts under a minimum-version pin extension. A same-hardware comparison against Sigstore-Cosign and python-TUF locates federated trust's per-verify cost between the two and its per-publisher storage footprint below both.

cs.CR↗
arXiv2026

Vision Token Manipulation Attacks on Cloud-Edge Inference of Large Vision-Language Models

Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers. In this process, intermediate vision tokens are transmitted from the edge to the cloud over a communication link, thereby exposing a new attack surface. We study vision token manipulation attack (VTM-Attack) under a black-box man-in-the-middle setting, where an adversary intercepts and manipulates a subset of transmitted vision tokens under a budget constraint. We propose four naïve attack strategies and an optimization-based token selection method. Experiments on 6 state-of-the-art LVLMs (3B-72B) across 4 benchmarks show that manipulating only 10\% of vision tokens can reduce accuracy by up to 88.31\%. These results reveal a critical vulnerability in cloud-edge LVLM inference.

cs.CR↗
arXiv2026

ComputeFHE: A Privacy-Preserving General-Purpose Computation Library

Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data while preserving data confidentiality. However, its practical applications remain limited by high computational costs and development complexity. This paper presents ComputeFHE, an open-source C++ library that facilitates the development of privacy-preserving applications based on the TFHE cryptosystem. The library provides encrypted integer and fixed-point data types together with arithmetic, logical, comparison, conditional, and oblivious array-access operations which allow developers to implement algorithms using a familiar imperative programming paradigm. ComputeFHE supports both conventional TFHE arithmetic based on standard two-input logic gates and an optimized Arithmetic Logic Unit (ALU) architecture utilizing FHE-friendly logic primitives. Experimental results demonstrate significant reductions in the number of required bootstrapping operations, achieving performance improvements of up to 3.9x for selected operations. In addition, the library includes a simulation mode that enables testing, debugging, and complexity analysis without performing actual cryptographic computations while providing circuit complexity and bootstrapping costs. Built on top of OpenFHE, ComputeFHE offers a practical and accessible framework for developing and evaluating privacy-preserving algorithms and applications.

cs.CR↗
arXiv2026

Towards Decentralized Registries for Assets Metadata Information

The effort to tokenize non-currency assets faces several hurdles, including the lack of a scalable decentralized computing infrastructure to manage asset-related metadata. While the centralized securities depository model has served the financial industry well for several decades, the vision of tokenization at a global scale requires new infrastructure that enables distributed control while protecting the integrity of asset-related metadata, regardless of where it is stored. In this paper, we discuss the decentralized artifacts metadata registry model for tokenized assets as a possible direction for the financial industry seeking to embrace tokenization. The artifacts metadata registries extend the function of the traditional CSD, and could in fact be a new type of service offered by CSDs around the world.

cs.DC↗
arXiv2026

From Preventive to Reactive: How AI Coding Assistants Transform Developers' Security Awareness

AI coding assistants are now central to professional software development, yet their impact on how developers think about and practice security remains poorly understood. While prior work has documented vulnerability rates in AI-generated code, a more fundamental question persists: how do these tools transform security awareness in authentic, ongoing development practice? We conducted semi-structured interviews with 15 professional software engineers and observed them completing security-relevant coding tasks with AI assistance, spanning 3 experience cohorts defined by their relationship to AI tools during professional formation. We find that AI coding assistants reorganize rather than eliminate security thinking, shifting it from the act of writing code to the act of reviewing it. This transition from preventive to reactive security is structurally encouraged by interaction models that frame code generation as a functional task, leaving security as an afterthought. Notably, none of our coding session participants specified security requirements in their initial prompts, even when they possessed the relevant knowledge, revealing a decoupling of security awareness from security behavior. We further document informal coping strategies developers had independently invented to manage AI security risk, none of which are supported by current tools or organizations, and find that the experience cohort did not reliably predict security performance. This paper contributes a practice-grounded account of how AI-assisted development reshapes the human side of secure coding, offering empirical foundations for the design of more security-aware tools, training programs, and organizational policies.

cs.HC↗
arXiv2026

SMCC-Empowered Digital Twins for Sensorless Monitoring in Large-Scale AI-Driven IoT Systems

The deployment of AI-driven Digital Twins (DTs) in large-scale Internet-of-Things (IoT) ecosystems demands continuous, high-fidelity synchronization between the physical environment and its virtual replica. Conventional approaches rely on dense sensor deployments, which introduce prohibitive costs in terms of hardware, energy, and network bandwidth. In this paper, we propose SMCC-DT, an integrated Sensing-Memory-Communication-Computation (SMCC) framework that enables sensorless monitoring of physical assets by exploiting Integrated Sensing and Communication (ISAC) waveforms at the 6G Edge. Under the SMCC-DT paradigm, a single radio signal simultaneously extracts environmental telemetry (Sensing) and delivers it to an Edge server (Communication), where a large-scale AI model is loaded into constrained memory (Memory) and executed (Computation) to update the DT state. We formulate the DT synchronization problem as a cross-layer optimization that jointly allocates transmit power, beamforming vectors, memory partitions, and CPU frequency to minimize the end-to-end synchronization latency subject to sensing accuracy, throughput, memory capacity, and computational budget constraints. Because the resulting mixed-integer nonlinear program is NP-hard, we design a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) agent, termed SMCCAGENT, that learns near-optimal resource allocation policies online. Extensive simulations over a 500-node industrial IoT testbed demonstrate that SMCC-DT reduces DT synchronization latency by 38.7% and total energy consumption by 27.4% compared to state-of-the-art orthogonal and compute-only baselines, while sustaining sensing accuracy above 95% and model inference throughput above 30 frames per second.

cs.DC↗
arXiv2026

A Secure, Manifest-Based Framework for Delegated Privilege Promotion

Large-scale enterprise software systems commonly run as unprivileged service accounts to enforce least privilege, yet still depend on a small set of privileged components -- such as executables with elevated ownership, permissions, or capabilities -- for narrowly scoped operations. This creates a persistent security and operational conflict during maintenance. Automated patching tools running without elevated privileges cannot safely update privileged components without either executing the entire patch with full administrative rights or requiring manual administrator intervention. We present a secure, manifest-based infrastructure for delegated promotion of privileged software components, deployed in production as part of a large-scale enterprise database system serving both cloud and on-premises installations. The design centers on a minimal privileged mediator that validates cryptographically protected metadata and allows an unprivileged process to promote only vendor-approved files. The system explicitly mitigates Time-of-Check-to-Time-of-Use (TOCTOU) attacks using file-descriptor-bound validation and promotion, supports offline key rotation and revocation, and enables zero-downtime self-update via atomic replacement.

cs.CR↗
arXiv2026

Statistical Effort Modelling of Game Resource Localisation Attacks

Evidence on the effectiveness of Man-At-The-End (MATE) software protections, such as code obfuscation, has mainly come from limited empirical research. Recently, however, an automatable method was proposed to obtain statistical models of the required effort to attack (protected) software. The proposed method was sketched for a number of attack strategies but not instantiated, evaluated, or validated for those that require human interaction with the attacked software. In this paper, we present a full instantiation of the method to obtain statistical effort models for game resource localisation attacks, which represent a major step towards creating game cheats, a prime example of MATE attacks. We discuss in detail all relevant aspects of our instantiation and the results obtained for two game use cases. Our results confirm the feasibility of the proposed method and its utility for decision support for users of software protection tools. These results open up a new avenue for obtaining models of the impact of software protections on reverse engineering attacks, which will scale much better than empirical research involving human participants.

cs.CR↗
arXiv2026

Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation

Current LLM-based services typically require users to submit raw text regardless of its sensitivity. While intuitive, such practice introduces substantial privacy risks, as unauthorized access may expose personal, medical, or legal information. Although prior defenses strived to mitigate these risks, they often incur substantial computational overhead and degrade model performance. To overcome this privacy-efficiency trade-off, we introduce Privacy-Preserving Fine-Tuning (PPFT), a novel training pipeline that eliminates the need for transmitting raw prompt text while maintaining a favorable balance between privacy preservation and model utility for both clients and service providers. Our approach operates in two stages: first, we train a client-side encoder together with a server-side projection module and LLM, enabling the server to condition on k-pooled prompt embeddings instead of raw text; second, we fine-tune the projection module and LLM on private, domain-specific data using noise-injected embeddings, allowing effective adaptation without exposing plain text prompts and requiring access to the decoder's internal parameters. Extensive experiments on domain-specific and general benchmarks demonstrate that PPFT achieves a striking balance between privacy and utility, maintaining competitive performance with minimal degradation compared to noise-free upper bounds.

cs.CR↗
arXiv2026

Feature Attribution in 5G Intrusion Detection: A Statistical vs. Logic-Based Comparison

With the rise of fifth-generation (5G) networks in critical applications, it is urgent to move from detection of malicious activity to systems capable of providing a reliable verdict suitable for mitigation. In this regard, understanding and interpreting machine learning (ML) models' security alerts is crucial for enabling actionable incident response orchestration. Explainable Artificial Intelligence (XAI) techniques are expected to enhance trust by providing insights into why alerts are raised. Under the umbrella of XAI, interpretability of outcomes is crucially dependent on understanding the influence of specific inputs, referred to as feature attribution. {A dominant approach to feature attribution statistically associates feature sets that can be correlated to a given alert. This paper investigates its merits against the backdrop of criticism from recent literature, in comparison with feature attribution based on logic. We extensively study two methods, SHAP and VoTE-XAI, as representatives of each feature attribution approach by analyzing their interpretations of alerts generated by an XGBoost model across three 5G-relevant datasets (5G-NIDD, MSA, and PFCP) covering multiple attack scenarios. We identify three metrics for assessing explanations: sparsity, how concise they are; stability, how consistent they are across samples from the same attack type; and efficiency, how fast an explanation is generated. Our results reveal that logic-based attributions are consistently more sparse and stable across alerts. More importantly, we found a significant divergence between features selected by SHAP and VoTE-XAI. However, none of the top-ranked features selected by SHAP were missed by VoTE-XAI. Finally, we analyze the efficiency of both methods, discussing their suitability for real-time security monitoring even in high-dimensional 5G environments (478 features).

cs.CR↗
arXiv2026

PolyJailbreak: Cross-Modal Jailbreaking Attacks on Black-Box Multimodal LLMs

Multimodal large language models (MLLMs) have become integral to a wide range of real-world applications by jointly reasoning over text and visual inputs. However, despite recent advances in safety alignment, MLLMs remain vulnerable to jailbreak attacks, where carefully crafted inputs can bypass safety mechanisms and elicit harmful responses. In this work, we investigate the security vulnerabilities of MLLMs in text-vision scenarios and propose a novel black-box jailbreak framework, named PolyJailbreak. We first identify a phenomenon, termed multimodal safety asymmetry, where visual alignment introduces uneven safety constraints across modalities and weakens overall robustness. We analyze attention dynamics and latent representations in MLLMs, revealing that visual inputs can disrupt cross-modal information flow and reduce the model's ability to separate benign and malicious intents. Motivated by these findings, we propose PolyJailbreak, which organizes the discovered vulnerabilities into a structured library of reusable Atomic Strategy Primitives to enable step-wise transformations from harmful intents to effective jailbreak inputs. Guided by these primitives, a reinforcement learning-based multi-agent optimization process automatically adapts attacks to the target model without access to internal parameters. Extensive experiments on a wide range of MLLMs demonstrate that PolyJailbreak consistently outperforms state-of-the-art jailbreak baselines, with an average improvement of 18.15% in attack success rate and a success rate exceeding 95% on commercial black-box models, including GPT-4o and Gemini.

cs.CR↗