Results show that unguarded and prompt-only baselines retain residual vulnerability under multi-turn adversarial trials, while Guarded-V2X consistently reduces intrusion acceptance success rates and eliminates observed unsafe completions in two-turn settings, without exceeding latency budgets for V2X semantic advisory paths.
Abstract
Vehicle-to-everything (V2X) systems increasingly incorporate large language models (LLMs) for semantic tasks such as message summarization, operator assistance, and decision support at roadside units and edge nodes. Although these components are not part of safety-critical control loops, they introduce prompt-level attack surfaces that are not addressed by traditional V2X security mechanisms focused on authentication and message integrity. This paper presents Guarded-V2X, an inline semantic guardrail architecture for securing LLM-enabled V2X services under real-time constraints. The proposed system integrates rule-based ingress filtering, a lightweight safety classifier, policy-constrained structured generation, trusted-only retrieval, and post-decision adjudication to enforce machine-checkable safety boundaries prior to downstream execution. Guarded-V2X is evaluated using a four-stage experimental pipeline encompassing intrusion vulnerability analysis, calibration and latency benchmarking, guardrail validation, and robustness under adversarial stress. Experiments are conducted on a V2X-aligned simulated dataset derived from RSU advisories, operator messages, and annotated V2X message summaries. Results show that unguarded and prompt-only baselines retain residual vulnerability under multi-turn adversarial trials, while Guarded-V2X consistently reduces intrusion acceptance success rates and eliminates observed unsafe completions in two-turn settings, without exceeding latency budgets for V2X semantic advisory paths.
This work constructs a dataset called CAV-STIXGen that maps CAV vulnerability descriptions to STIX domain objects, and analyzes CWE and MITRE ATT&CK co-occurrences to identify recurring threat patterns in the CAV domain, demonstrating how AI-assisted vulnerability-to-STIX translation can automate threat intelligence and prioritize defense in transportation security.
Md Erfan, A. Ryan, Md. Kamal Hossain Chowdhury et al.· 0 citations
CyberLLM is presented, a multi-agent, LLM-orchestrated framework that autonomously detects vulnerabilities and executes remediations under a formal, runtime safety guard, and indicates that LLM agents can perform useful autonomous cyber-defense when wrapped in a deterministic, auditable safety envelope.
Nenad Petrovic, Oussama Jeddou, Feres Ben Fraj et al.· 0 citations
Large vision--language models (VLMs) can reason step by step about complex visual scenes, but this open-ended, autoregressive chain-of-thought (CoT) approach is poorly suited to safety-critical, rule-governed settings such as industrial surveillance, where decisions must be bounded, deterministic, and low-latency. Because CoT inference cost scales jointly with reasoning length and the number of concurrent streams, it creates a throughput bottleneck that precludes the real-time, multistream monitoring required for industrial accountability. Here we present MonitorVLM-v2, a deployment-oriented framework that recasts VLM-based safety assessment as probabilistic inference over a finite regulatory decision space, compressing multimodal reasoning into single-step rule-ID predictions and reducing decoding from a variable-length sequence to a single token. We introduce symbolic policy optimization (SymPO), a novel contrastive policy optimization algorithm that sharpens decision boundaries within this finite symbolic space, together with an entropy-driven triage mechanism that routes uncertain predictions to human reviewers for expert confirmation. In a four-month prospective deployment across 10 concurrent camera feeds in an operational underground mining facility, MonitorVLM-v2 achieved a 19.45-fold increase in inference speed and identified 2.78 times as many confirmed violations as the site's routine manual inspection workflow, demonstrating the practical value of compressed symbolic decision-making for real-time, auditable industrial monitoring.
Jiang Wu, Sichao Wu, Yinsong Ma et al.· 0 citations
Logistics Cyber-Physical Systems (LCPS) generate large volumes of regulatory and operational texts that encode early signals of safety risks. Converting short, noisy, and domain-specific records into actionable intelligence is difficult due to industrial semantic drift and the limited auditability of black-box predictors. This paper proposes Neuro-Symbolic Logistics Risk Awareness (NS-LRA), a dual-channel framework that integrates lightweight semantic perception with constraint-aware topological reasoning. NS-LRA first maps raw texts to a standardized schema of $K=20$ risk nodes using a dual-weighted embedding mechanism that combines TF-IDF and Word2Vec to mitigate short-text sparsity. It then constructs a directed risk graph by fusing co-occurrence evidence with a domain constraint mask, and derives hierarchical propagation via ISM level partitioning with deep-driver identification via MICMAC analysis. We evaluate NS-LRA on $N=8,435$ records, validated against an annotated subset $(\mathcal{D}_{\text{ann}}=1,500)$) with Fleiss' $\kappa=0.82$ and an expert-defined gold graph. NS-LRA achieves Micro-$\mathrm{F} \mathrm{1}=\text{0. 8 7 9}$ for risk mapping and approximately $22 \times$ lower perrecord CPU latency than fine-tuned BERT on the same test split under the same environment. For topological inference, NS-LRA reports $\text{E P}=\text{0. 9 2 4}$ and $\text{T C}=\text{0. 9 5}$ against the gold graph. These results indicate that NS-LRA can provide an efficient and traceable pipeline for proactive risk governance in LCPS.
Ke Huang, Yan Liu, Bin Guo et al.· Annual International Compute...· 0 citations
This work proposes a stateful, multi-agent validation pipeline that eradicated cross-phase hallucinations and proves adversarial auditing enables LLMs to reliably synthesize zero-error MBSE architectures.
Aleksei Velsh, Nenad Petrovic, Alois Knoll· 0 citations
Working baseline levels of capability are provided with respect to current LLM-based solutions in 6G mission-critical and public safety contexts, and specific research directions to advance LLM-driven cybersecurity toward robust, adaptable, explainable, and life-safety-aware solutions are mapped out.
Siva Sai, Bhuvan Arora, Vineet Suri et al.· IEEE Open Journal of the Com...· 1 citation
