Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.41050

Closing the Applied Cybersecurity Gap in Critical Infrastructure: A Cyber-to-Physical Security Assessment Framework for Water Systems

Halil Dursunoglu·Kaan Sulkalar
View original source ↗Read source document ↗Export metadata ↓

Abstract

The integration of information technology and operational technology in water infrastructure creates pathways through which cyber compromise may affect physical service delivery. This study revisits the Applied Cybersecurity Gap (ACG) framework by evaluating an experimental Applied Cybersecurity Gap Index (ACGI) alongside the Cyber-to-Physical Propagation Rate (CPPR), defined as the proportion of initially compromised trials that produce a specified physical-impact outcome. The expanded evaluation comprises 2,160 synthetic trials across three water-network benchmarks, including EPANET Example Network 3 (Net3), and couples declared cyber-attack progression rules with hydraulic simulations. A source-trained machine-learning model evaluated on 247 compromised Net3 trials achieved ROC-AUC 0.772, PR-AUC 0.775, and F1 0.733, compared with 0.661, 0.642, and 0.704 for a scenario-only baseline. The configuration-cluster bootstrap interval for the ROC-AUC difference was 0.038--0.183. A separate 720-run Net3 hydraulic operating-point sensitivity analysis achieved the study's exploratory baseline pressure criterion in all reruns, preserved 717 of 720 physical-impact labels, and produced ROC-AUC 0.791 with the frozen source-trained model. Additional analyses examine the sensitivity of CPPR to the physical-impact threshold and assumed control-blocking probabilities, and demonstrate that the ACGI--CPPR association is partly induced by the simulation design rather than independently validated. The findings establish a reproducible simulation-based assessment workflow, but do not constitute field calibration of cybersecurity-control effectiveness or prediction of real-world utility incidents.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Halil Dursunoglu, Kaan Sulkalar. 2026-10-29. Closing the Applied Cybersecurity Gap in Critical Infrastructure: A Cyber-to-Physical Security Assessment Framework for Water Systems. https://arxiv.org/abs/2610.41050

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A Survey on Reconfigurable Intelligent Surfaces Towards Practical Deployments: Security and Privacy Perspectives

Reconfigurable Intelligent Surfaces (RIS) have emerged as a transformative technology capable of reshaping wireless environments through dynamic manipulation of electromagnetic waves. Although RISs have been widely studied for communication, sensing, and localization, their security and privacy implications are still fragmented across application-specific studies, prototypes, and testbeds. This survey provides a deployment-oriented review of RIS applications, with a focus on the security and privacy threats, vulnerabilities, and defensive strategies relevant to practical use. We analyze scenarios with two types of systems (with and without legitimate RIS) and two types of attackers (with and without malicious RIS), and clarify how RIS introduces new attack surfaces to practical systems, including eavesdropping, jamming, and spoofing attacks. In response, we review defenses against RIS-related attacks in these systems, such as protocol- and algorithm-level protections, unauthorized RIS detection, and interference/disruption-based mitigation. We also discuss scenarios in which the legitimate user applies an additional RIS to defend against attacks. To support future research, we also curate papers and available open resources at https://awesome-ris-security.github.io/. By highlighting RIS's functionality and its security/privacy challenges and opportunities, this survey aims to guide researchers and engineers toward the development of secure, resilient, and privacy-preserving RIS-enabled practical wireless systems and environments.

cs.CR↗
arXiv2026

Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code

Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding (GCD) has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this paper, we reveal a counterintuitive risk: this reliability-oriented technique can itself become an attack surface. We uncover a new jailbreak attack, termed CodeSpear, that exploits GCD to induce LLMs into generating malicious code. Our experiments show that simply applying a benign code grammar constraint can effectively jailbreak LLMs. To address this vulnerability, we propose CodeShield, a safety alignment approach that robustly preserves safe behavior even under attacker-controlled grammar constraints. CodeShield aligns the model in the code modality by teaching it to generate honeypot code under GCD. Such code is semantically harmless, so it does not implement the malicious request, and structurally diverse, so it is difficult to suppress through grammar tightening. At the same time, CodeShield still preserves natural-language refusals when natural language is available. Experiments on 10 popular LLMs across 4 benchmarks show that CodeSpear outperforms representative jailbreak baselines and increases the attack success rate by more than 30 percentage points on average. CodeShield also restores safety under CodeSpear while preserving benign utility. Our findings reveal a fundamental risk of GCD and call for greater attention to its potential security implications.

cs.CR↗
arXiv2026

Jaguar: Fast Private Neural Network Inference with Power-of-Two Homomorphic Arithmetic

Hybrid HE/2PC private neural network inference remains bottlenecked by prime-modulus homomorphic arithmetic in convolution and by a precision flow that runs ReLU at doubled bitwidth before invoking a separate truncation protocol. We present Jaguar, a system built on a single design choice--a power-of-two ciphertext ring--that addresses both. The choice enables SPA-Conv, a coefficient-domain convolution kernel that replaces NTT-centric polynomial multiplication with Scalar-Polynomial Accumulation, and an exact ciphertext-side truncation by local right shifts that lets ReLU run directly at the target fixed-point precision and eliminates the post-ReLU truncation protocol. Where NTT remains genuinely useful--at the client, for the single polynomial multiplication during decryption--we recover it through an auxiliary NTT prime, preserving the power-of-two protocol substrate while keeping decryption O(N log N). On ImageNet-scale ResNet-18, ResNet-50, and MobileNetV2 with AVX disabled, Jaguar achieves 2.07-3.72x lower end-to-end latency than Cheetah and 2.16-3.36x lower than Rhombus, with 1.16-1.76x lower communication than Cheetah.

cs.CR↗