2026· IEEE Transactions on Network and Service Management· Vol 23, pp. 6191-6205· 0 citations· 70 references
Abstract
Modern services are awash in Application Programming Interfaces (APIs), yet most security pipelines end at pre-deployment testing using fuzzers and scanners. This leaves a run-time gap where payload obfuscation and other evolving request-visible misuse patterns outpace static rules and slow retraining cycles. We present HotPatchCaps, an expert-in-the-loop runtime framework that closes this gap by hot patching expert knowledge into a capsule architecture without retraining. HotPatchCaps fuses Term Frequency–Inverse Document Frequency (TF–IDF) statistics on request tokens with security cues such as parameter names, encodings, and payload substrings, and employs slot-controlled routing to amplify semantically relevant evidence into interpretable capsule activations. New rules arrive as lightweight runtime patches that can be injected on the fly, aligning with operational practice while preserving the generalization of learned models. We evaluated the CSIC 2010 dataset and the ATRDF 2023 dataset in both in-distribution and zero-day settings against classical machine learning (ML) and deep baselines. Experimental results demonstrate that HotPatchCaps consistently improves accuracy and recall at competitive precision and remains robust under label noise and schema drift. By turning expert knowledge into patchable capsule priors, HotPatchCaps provides a practical path from testing to on-call defence for API-centric systems.
A deployment-minded pipeline that integrates JSON-aware canonicalization that standardizes benign representational variance while preserving attack-relevant semantics, with the largest reductions on JSON wrapping and on multi-step chains such as homoglyph ?
Ali Taqi, Hassan Hassan, Ghaidaa Abdulsaheb· Journal of Applied Science a...· 0 citations
Large language models (LLMs) embedded in enterprise workflows cannot structurally distinguish legitimate instructions from adversarial ones in the same token stream, making prompt injection OWASP's top LLM risk for two consecutive editions a persistent threat across direct and indirect vectors. This paper presents PromptShield-RT, a layered, real-time, model-agnostic framework combining input normalization and provenance tagging, lexical-heuristic pattern matching, a statistical classifier, structural anomaly features, and calibrated risk fusion, with policy-driven mitigation (allow/sanitize/quarantine/block) and an explainable, adaptive-feedback mechanism for SOC workflows. We construct an original evaluation corpus, SynPI-Bench (n = 450, six categories), and a template-disjoint held-out generalization set (n = 31) with novel phrasings, obfuscation encodings, and adversarial hard-negative benign text. Using template-grouped 5-fold cross-validation, the fused pipeline achieves 92.4% accuracy (F1 = 0.930, AUC = 0.990), outperforming heuristic-only (57.0%) and naive-averaged (59.2%) baselines, while a lexical classifier reaches 85.9% with lower precision. We report a pronounced generalization gap on the held-out set (48.4% accuracy, 90% false-positive rate on hard negatives), quantifying a known limitation of surface-lexical defenses. The pipeline achieves sub-millisecond P95 latency (0.266 ms), within typical 50 ms enterprise SLAs. We situate PromptShield-RT relative to structural, architectural, and guardrail-product defenses, arguing for layered, defense-in-depth architectures, with reproducible code provided.
Fatimah Alhamzawi· Al-Noor Journal of Engineeri...· 0 citations
This work shows that a malicious developer can pair a benign-looking wrapper with crafted metadata to deterministically alter post-generation behavior without modifying model weights, training data, or inference backend, and introduces TIF-BAH, a lightweight middleware defense that verifies wrapper integrity and records behavioral attestations during inference.
Nokimul Hasan Arif, Qian Lou, Meng Zheng· 0 citations
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% ($\Delta = 0.669$), a gap that holds for every model and for six of seven classes, from credential and network-policy ($\Delta = 0.95$) to role-based access control ($\Delta = 0.31$); a topology-independent control exhibits no such effect ($\Delta = 0.0$), 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.
Distributed Denial-of-Service (DDoS) attacks across cloud, edge, and Internet-of-Things infrastructures have intensified the need for detectors that are accurate, computationally efficient, and deployable at resource-constrained monitoring points. Much of the intrusion-detection literature implicitly links detection quality to architectural depth, larger parameter spaces, and richer learned representations. This paper examines a narrower hypothesis: for binary flow-level DDoS detection, a carefully engineered shallow pipeline can retain high detection quality while maintaining a compact computational footprint. We propose ShallowNet, a single-hidden-layer neural detector integrated with leakage-aware data partitioning, training-only normalization, principled feature selection, class-imbalance-aware optimization, early stopping, and calibration-guided threshold selection. The evaluation extends beyond standard held-out accuracy by incorporating cross-day temporal transfer, leave-one-attack-family-out robustness testing, controlled baseline comparison, systematic pipeline ablation, and probability-calibration analysis. The results show both the empirical utility and the limits of the approach: class concentration in widely used benchmark datasets can make aggregate metrics overly optimistic, and generalization degrades most sharply when the test traffic contains attack signatures absent from training. These findings suggest that, in lightweight binary flow-level DDoS detection, disciplined pipeline design can be as important as model depth and that stress-tested evaluation should accompany high benchmark accuracy claims.
Ali Alfatemi, Mohamed Rahouti, A. Chehri et al.· IEEE Transactions on Network...· 0 citations
This work proposes Routing-based On-Policy Distillation (ROPD), a novel realignment framework that models the divergence between aligned and compromised output probability distributions rather than fitting specific prompt templates, establishing a new standard for robust LLM realignment.
Yongjian Guo, Wanlun Ma, Lingyu Shen et al.· 0 citations