CyberSec Research

Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads. Recent work suggests that large language models (LLMs) can automate cluster security remediation, generating configuration patches from Kubernetes Security Posture Management (KSPM) findings without human authoring. Such systems, however, prompt the model with each finding in isolation from the live service call graph, assuming general hardening knowledge suffices. This assumption breaks down whenever a patch must preserve a runtime service dependency invisible to the model: an otherwise compliant fix then carries a destructive functional blast radius, crashing downstream callers or silently severing call edges across the cluster. Whether live cluster context improves patch correctness has not been measured under controlled conditions across multiple dependency classes. We introduce KuTIE (Kubernetes Topology Intelligence Engine), which builds a live cluster context from Istio call edges, Trivy KSPM findings, and the service-account bindings a workload reads, and conditions LLM patch generation on it. It is evaluated on VulnCare, a purpose-built 36-deployment, four-namespace healthcare cluster with 31 injectable findings across seven dependency classes, each labelled by topology dependence against cluster ground truth. Across 248 trials, topology context raises topology-dependent patch correctness from 11.1% to 78.0% ($Δ= 0.669$), a gap that holds for every model and for six of seven classes, from credential and network-policy ($Δ= 0.95$) to role-based access control ($Δ= 0.31$); a topology-independent control exhibits no such effect ($Δ= 0.0$), isolating the result from generic prompt enrichment. Supplying the live service-call graph and the service-account bindings it exposes thus improves remediation of topology-dependent findings well beyond scanner-only context.

When a language model receives conflicting instructions from different priority levels, which one does it actually follow? This question lies at the heart of reliable LLM deployment. Existing benchmarks answer this only partially, often focusing on a single hierarchy edge or adapting public datasets with limited tool-use coverage. We present IH-Benchmark, a conflict-centered benchmark for instruction-hierarchy robustness across direct system-user conflicts (S>U) and tool-mediated user-tool (U>T) conflicts. IH-Benchmark is built from a human-authored taxonomy of 44 constraint families across generic, health, finance, retail, and coding settings, and evaluates scenarios with a uniform binary pass/fail protocol combining a predicate DSL with category-scoped LLM judges. Across 37 evaluated models, hierarchy compliance ranges from 98.2% to 20.5%. We find that strong S>U compliance is not a reliable proxy for U>T robustness: several models preserve system constraints under direct user conflict but degrade sharply when conflicting instructions appear in tool outputs. Constraint hardening also reveals a split between models: some failures are largely fixed by stronger warnings, while others persist across all strictness levels. Finally, the most revealing failures are often subtle rather than overtly dangerous; models resist unauthorized purchases or bulk ticket closure more reliably than injected disclaimers or small factual distortions. These results suggest that instruction-hierarchy robustness is not a single capability, but a set of behaviors that must be evaluated across conflict surfaces, constraint types, and attack presentations.

EEG-based community sensing programs are emerging globally as a tool to leverage aggregated brain data to gain insights into attentiveness of students and employees. But these programs raise privacy concerns because EEG signals contain sensitive personal information. Differential Privacy (DP) can protect individuals while preserving aggregate statistics yet applying DP to EEG data is challenging as it requires user-level noise generation, which increases power and latency. Besides, most commercial EEG headsets cannot be modified to add such noise. We propose E-MagDiP, a framework that uses an external radio to transmit RF signals onto EEG headsets, perturbing signals at acquisition to induce DP noise. To the best of our knowledge, E-MagDiP is the first framework to use RF signals for privacy instead of attacks, enabling practical DP for EEG community sensing without any user-level modification.

LLMs are increasingly deployed in real-world applications, making inference efficiency and service reliability critical concerns due to their substantial computational costs. However, the autoregressive generation mechanism of LLMs enables malicious prompts to manipulate generation behaviors, inducing excessive token generation that amplifies computational consumption and threatens service efficiency. Existing methods mainly rely on adversarial suffixes or explicit extension instructions, which introduce detectable behaviors and limit their applicability. In this paper, we reveal a previously unexplored vulnerability caused by persona consistency in LLMs, where models maintain assigned roles and reproduce corresponding behaviors even when they result in inefficient reasoning and excessive generation. Based on this observation, we propose RolePlay, a task-aware dynamic persona alignment framework that constructs adaptive personas to naturally induce inefficient yet semantically coherent behaviors for inference cost amplification. Extensive experiments across multiple LLMs and diverse task datasets demonstrate that RolePlay consistently outperforms existing inference extension methods, achieving an average token amplification of up to \bm{$7.64\times$} and a maximum token amplification ratio of \bm{$207.64\times$}. Our findings identify persona conditioning as a new attack surface for LLM inference efficiency and offer a new perspective on computational cost amplification.

Leader-based BFT protocols finalize through their leaders: a view whose leader is crashed or slow finalizes nothing, and the timeout that ends it admits no good setting. A conservative timeout turns every crashed leader into a long stall; an aggressive one voids the views of leaders that are merely slow. Either way the expired view is wasted, and this trade-off, not the good case, governs tail latency. Hermes makes expired views finalize. Hermes is a two-round rotating-leader protocol for $n=5f+1$ processes under partial synchrony built on prefix consensus: votes carry values ordered by a prefix relation, and quorums require comparability rather than equality. Every process broadcasts a justified proposal at view start and casts a single vote, for the leader's proposal upon delivery or for a fallback proposal at the timeout; there are no nullify votes. A timely honest leader finalizes its full proposal in $2δ$. Otherwise, any $n-f$ votes, which need not match, finalize the heaviest common prefix. We instantiate Hermes as a finality gadget over an available chain and over Autobahn-style multi-lane dissemination, where parent-relative delta tipcuts with explicit skips keep independent proposals comparable and sender-indexed erasure-coded amplification keeps complexity at $O(n^2\hat{m}+λn^3)$ bits and $O(n^2)$ messages per view, where $\hat{m}$ is the number of concurrent dissemination lanes in Autobahn and $λ$ the security parameter.

Autonomous Network Levels 4-5 require AI agents to invoke tools across vendor boundaries without human oversight, yet existing management standards lack a standardized mechanism for cross-vendor trust visibility. When a tool from Vendor B is compromised, agents from Vendor A continue invoking it -- unaware of the trust degradation -- causing cascading service impact. We present AgentToolMO, a proposed 3GPP NRM information model for agent tool trust management. The model comprises: a formally defined trust state machine with provable graduated enforcement, damped cascade propagation with bounded convergence, cross-vendor trust notifications via existing Management Services (MnS) interfaces, and retroactive impact assessment through NRM dependency graph traversal. Simulation-based evaluation across multi-vendor topologies shows that standardized cross-vendor notifications reduce blast radius from hours-scale undetected propagation to near-real-time containment bounded by MnS notification delivery, with cascade convergence guaranteed in bounded iterations and sub-linear notification scaling across vendor domains. The framework operates within existing 3GPP management infrastructure, leverages existing protocols, and provides a standardization pathway for trustworthy multi-vendor autonomous network management.

LLM provenance testing asks whether a suspect LLM belongs to the same lineage as a source. Existing black-box methods largely infer this relationship from response-level characteristics, but these characteristics may shift under adaptation or deployment even when the underlying meaning remains unchanged, weakening the reliability of provenance evidence. To address this limitation, we introduce induced decision regions by mapping open-ended outputs into a finite decision space, thereby abstracting away surface-form variation and reframing provenance testing as measuring the inheritance of decision regions. Empirical analysis shows that the source's induced regions are preserved more strongly in related models than in unrelated models. Building on this signal, we propose Stemma, a practical black-box LLM fingerprinting method that operationalises stability, robustness, and specificity as complementary probe-selection principles for reliably estimating induced decision region inheritance. Across 770 source-suspect pairs drawn from 56 public checkpoints and spanning diverse model-weight transformations, Stemma achieves 0.967 AUC and 87.8% TPR at 1% FPR, substantially outperforming four representative baselines. It further achieves 0.995 AUC and 93.5% TPR at 1% FPR on 1,260 pairs covering 91 deployment instances, demonstrating robustness to diverse inference-time deployment settings.

This paper describes the participation of team "Go To Germany" in the ImageCLEF 2026 Deepfake Detection and Generation Task. For the image generation task, we employ FLUX.1-dev with PuLID for identity-preserving face synthesis, combined with a multi-model PGD adversarial attack targeting 12 detectors simultaneously (DiffJPEG-in-loop, MI/DI/EoT, adaptive weighting, two-stage warm-start). Our approach achieved 90% evasion against organizer detectors and 57.6% against participant detectors, with a final generation score of 0.4170. For the image detection task, we combine two complementary detectors - SigLIP+DINOv2 for AI-generated images and GenD-DINOv3 for face manipulations - in a max-probability ensemble, achieving 99.4% accuracy on baseline deepfakes but suffering from high false-positive rates on real images, resulting in a final detection score of 0.6986. Beyond the official submission, we conducted a self-initiated investigation of purification-based adversarial detection, comparing three families of detection signals across six detectors that share a CLIP ViT-L/14 backbone. We find that raw $|Δ\text{logit}|$ under median-3 purification, applied through the EFFORT detector, separates adversarial inputs from clean inputs with AUROC 0.81-0.98 across four adversarial source types - a finding that refutes the simple backbone-preservation hypothesis and exposes a sharp JPEG-quality cliff at Q70 where the signal collapses.

The emergence of the recent outstanding capabilities of Arabic Language Models has opened doors for exposing their vulnerabilities. One of the major security risks associated with such Natural Language Processing models is adversarial attacks. These attacks can deceive the model into the wrong prediction, raising critical model security and safety concerns. This study aims to assess the robustness of five state-of-the-art Arabic Language Models under a distinct set of Arabic adversarial attacks applied at various levels of granularity and using different example generation strategies. We also explore a defense technique based on adversarial training to enhance model robustness. The results show that insertion of diacritics can reduce the accuracy of some models by 92% while maintaining a low perturbation distance. For word-level attacks, manipulating Arabic conjunctions preserves high semantic similarity scores, low perturbation distance, and leads to an accuracy degradation of up to 58%. For sentence-level attacks, paraphrasing proves its effectiveness by an average reduction of 76% in the victim models' performance. While adversarial training improves overall resilience, with MARBERT being the most robust and AraBERT showing the greatest relative gains, challenges persist, particularly against character-level noise. These findings highlight both the potential and limitations of current defense strategies in morphologically rich languages like Arabic.

The problem: Cybersecurity practice runs simultaneously across analysts, teams, organizations, sectors, and regulators, co-evolves with adversaries, and increasingly blends human and algorithmic decision-making. The theories applied to it operate at single organizational levels and cannot explain why organizations with broadly similar controls differ sharply in resilience. This paper: We develop STARC (Structuration Theory Adaptation for Resilient Cybersecurity), a framework for locating where cybersecurity practice succeeds or fails structurally. It extends Giddens' Structuration Theory with three innovations, Multi-Level Adversarial Agency, Threat-Adaptive Structuration, and Material-Agential Structural Properties, across five structure-agency triads. Evidence base: STARC is illustrated through re-analysis of three financial organizations, an Australian, an Indonesian, and a Malaysian bank, across 20 interviews from SOC analysts to senior executives, selected as diverse insourced and outsourced configurations rather than as a comparison of equivalents. Cybersecurity contribution: STARC offers a structural account of why differently resourced and outsourced organizations differ in resilience, and a vocabulary for diagnosing incident-response breakdown across levels, tempos, and the human-algorithm authority boundary that single-level frameworks leave invisible. Theory and outputs: It extends Structuration Theory to adversarial, multi-level, and hybrid human-algorithmic contexts, and yields seven testable propositions linking structuration to resilience, offered for future testing.

Quantum error mitigation (QEM) is an essential tool for mitigating hardware noise without incurring space overhead. Yet, its reliability depends on modeling, calibration, and implementation, leaving end-to-end security on untrusted quantum hardware unresolved. We address this problem by introducing verifiable blind probabilistic error cancellation (VBPEC), the first secure verification protocol against a fully malicious adversary that integrates QEM. VBPEC brings probabilistic error cancellation (PEC), a widely studied QEM technique, within the scope of composable security by formalizing delegated mitigation as a cryptographic resource in the abstract cryptography framework. The protocol performs PEC with perfect blindness and an exponentially small security error. VBPEC retains the absence of quantum-space overhead from recent statistically-secure verified quantum computation protocols and from PEC. The only overhead takes the form of additional repetitions due to the QEM procedure. To achieve this, we extend trap-based verification from deterministic pass/fail checks to statistical tests that benefit from QEM and develop a new proof technique that integrates the corresponding additional deviation sources. Rather than merely tolerating honest noise below a fixed threshold, VBPEC actively cancels it, enabling correctly mitigated estimates to be accepted with high probability without compromising security. Our framework thus establishes an essential route towards secure, reliable, and practical delegated quantum computation on near-future quantum hardware: VBPEC fundamentally improves the practicality of verification.

Semantic communication systems such as deep semantic communication (DeepSC) offer high efficiency but are vulnerable to adversarial attacks on their underlying neural networks. We address a physical-layer man-in-the-middle (MitM) threat in which an adversary injects perturbations into the transmitted signal to distort its meaning. We propose SignDeepSC, an architectural defense that achieves adversarial robustness without requiring explicit adversarial example generation during training. The approach is built on a perceiver-inspired semantic signature, a compact vector summary of the source features transmitted over a separate low-rate auxiliary channel. This signature is used by a self-repairing decoder that leverages cross-attention to correct distortions and can additionally drive a scrambler that shuffles the feature layout. We evaluate SignDeepSC over Rayleigh fading and additive white Gaussian noise channels under both single-step fast gradient sign method (FGSM) and iterative projected gradient descent (PGD) attacks. Under PGD ($ε= 0.7$), at 12~dB signal-to-noise ratio with Rayleigh fading, SignDeepSC achieves a bilingual evaluation understudy (BLEU-4) score of 0.237 and bidirectional encoder representations from transformers (BERT) sentence similarity of 0.646, outperforming all baselines without degrading clean-channel performance, when the signature channel is well protected.

Software engineering teams now deploy AI coding agents (Cursor, Claude Code, GitHub Copilot) as first-class productivity tools, installing domain-specific skill files to tailor agent behavior to project APIs, framework conventions, and organizational workflows. These complex Markdown files are easily downloaded from public registries with a single npx skills add command and no real security screening, representing a novel supply-chain attack surface: a malicious skill file can silently reprogram agent behavior, exfiltrating credentials, injecting backdoors into generated code, or redirecting agent actions to attacker-controlled endpoints. The threat is not hypothetical: recent reports document hundreds of malicious skill packages in public registries, including organized campaigns that distributed credential-stealing infostealers via fake productivity skills. No systematic toolchain defense exists for this attack surface. We present SkillGate, a deployable security gateway that screens AI skill packages before coding agent installation. SkillGate uses a hybrid regex-prefilter + LLM-judge pipeline: safe-signal files bypass the LLM entirely (skip savings); flagged files have only their matched snippet windows sent to the judge, not the full content (snippet savings). We answer four research questions covering detection effectiveness, screening cost, runtime overhead, and false positive behavior on the SkillsBench benchmark against two existing tools. On SkillsBench (n=1,650, 9.1% malicious), SkillGate achieves F1=0.817, FPR=1.13% while reducing LLM input tokens by 77% vs. full-file screening, and outperforming existing tools by 5-6x on threshold-independent AUPRC (0.830 vs. 0.144/0.162).

We present a reproducible pipeline for mapping Common Vulnerabilities and Exposures (CVEs) to MITRE ATT&CK Enterprise techniques from free-text vulnerability descriptions. Rather than relying on the CWE->CAPEC->ATT&CK derivation chain, whose table-expansion artifacts we quantify, we train a multi-label classifier on a curated gold dataset of 1,207 CVEs from expert MITRE Center for Threat-Informed Defense mappings. The resulting model approximately doubles recall@5 compared with a zero-shot embedding-similarity baseline and improves every ranking metric. We then investigate whether LLM-assisted labeling can extend the gold dataset. Initial experiments suggest contradictory conclusions: a single run indicates degraded performance, while averaging over five random seeds suggests a small gain. However, an independent replication and an expansion-size study (100--984 additional CVEs) show that the apparent improvement is an evaluation artifact. LLM-generated labels, with approximately 0.39 agreement with expert annotations, provide no reliable improvement at any expansion size and reduce rare-technique coverage at around 1,000 added CVEs (macro-F1 decreases by 0.04). The root cause is evaluation noise. Selecting checkpoints on a small test split effectively maximizes over many noisy evaluations, producing recall@5 differences of up to 0.05 between otherwise identical runs. Using a corrected protocol based on validation-split checkpoint selection, the gold-only model achieves recall@5 of (0.673 \pm 0.019), and repeating the decisive experiment confirms the null result for LLM expansion. A final scaling study shows that additional expert-curated data consistently improves performance, whereas LLM-labeled data does not, indicating that the classifier is limited by label quality rather than dataset size. All datasets, models, code, and training logs are publicly released.

Decentralized manufacturing faces a pre-contractual impasse: a Provider cannot price a service accurately without inspecting the design file, yet the Consumer cannot share that file without exposing intellectual property. We introduce the Optimistic Verifiable Claim (OVC), a blockchain protocol that lets a Consumer publish a verifiable claim about a concealed design (such as the material it consumes) and a Provider price and bid on it without seeing the design. The claim is committed when the service is posted and stands unless the selected Provider challenges it; a challenge triggers a deterministic on-chain check that exposes any dishonesty, and the design is disclosed only to settle a dispute, never on the honest path. We implement four checks (authorized key access, delivery-channel integrity, syntactic conformance, and declared material consumption) in Solidity and measure them on a real 6.41 MB G-code file, the 3DBenchy, across Ethereum, Arbitrum, and opBNB. Every service incurs the cost of posting the encrypted design, with or without a dispute. For the 3DBenchy, the no-dispute outcome costs \$7,207 in up to 9 hours on Ethereum, \$288 in 3 min on Arbitrum, and \$2.87 in 2 min on opBNB, and a fully contested dispute costs \$49,660 in up to 57 hours on Ethereum, \$1,988 in 19 min on Arbitrum, and \$19.73 in 13 min on opBNB. Costs and times grow with size: for a 50 MB industrial design, an undisputed service reaches \$56,173 and up to 3 days on Ethereum against \$22.36 and 16 min on opBNB, and a fully contested dispute reaches \$488,440 over up to 18 days on Ethereum against \$195 and 1.6 hours on opBNB. Of the four, the material-consumption check is the costliest, its predicate being the most expensive to evaluate on-chain. OVC makes confidential, claim-based bidding economically feasible on Arbitrum and opBNB, but not on Ethereum at industrial scale.

Recent training-time defenses against neural backdoors isolate a benign subset from poisoned training data, to learn a backdoor-free model from it. In this paper, we formulate this defense strategy as a coreset selection problem, giving rise to so-called "Anti-Backdoor Coreset Selection." Since poisonous samples have (a) lower prediction uncertainty and are (b) less frequent than benign samples, coreset selection naturally focuses more on samples associated with benign functionality than the backdoor functionality. We use the Cumulative Entropy as selection criterion to further facilitate this effect. The metric tracks the learning dynamics of training samples and allowing us to select benign samples with high informativeness for the coreset. Additionally, we unlearn the chosen samples in each epoch to facilitate the separability between benign and poisonous samples. Together, this yields an exceptionally effective training-time defense that constructs a benign coreset to train a backdoor-free model. Unlike prior defenses that compromise natural accuracy and fail against certain attacks, our method mitigates backdooring attacks consistently with a negligible impact on natural performance.

Vision--Language Models (VLMs) are increasingly deployed through a model supply chain in which pretrained checkpoints, architecture definitions, text encoders, and exported computation graphs are distributed by third parties and reused across downstream services. This reuse model creates a security-critical trust boundary: VLM deployments inherit not only learned parameters but also executable behavior encoded in shared model artifacts. In this paper, we show that a malicious provider can exploit this trust boundary by embedding architectural backdoors into VLM supply chains through representation steering. Our attack introduces dormant steering logic into the model architecture through a trigger-gated additive modification of an intermediate representation, without poisoning training data, controlling downstream fine-tuning, or modifying prompts at deployment time. When the trigger is absent, the modification reduces to zero and the model follows its normal computation, preserving clean utility. When the trigger is present, a steering direction shifts the internal representation toward an attacker-defined objective. We evaluate the attack across multiple VLM families and downstream tasks, including visual question answering, text-to-image generation, retrieval, and semantic response biasing. The results show that the proposed architectural steering backdoor compromises integrity, safety enforcement, and ranking fairness while preserving normal behavior on clean inputs. We further show that shared VLM artifacts can carry dormant steering logic against downstream services, and we propose an auditing defense that inspects the executable logic distributed with model artifacts rather than only their learned weights.

We present LEAKFORGE, a device-agnostic framework that converts cross-device accelerometer eavesdropping into a physics-guided data-generation problem. Crucially, device-specific leakage is not arbitrary; its dominant variation lies within a constrained family of audio-to-accelerometer transfer functions. LEAKFORGE samples this family to synthesize large-scale, device-diverse accelerometer traces from ordinary speech, explicitly modeling electromechanical transfer, structural resonances, filtering, and aliasing. An eavesdropping model trained entirely in this synthetic domain can then be applied directly to traces from previously unseen smartphones.