AI-Enhanced SaaS Reliability: LLM-Based Log Parsing, OOV Detection, and Early Failure Prediction

Transformer-Based Multimodal Intelligence for Software Defect Detection: A Cloud-Native LLM Framework with Explainable AI for Digital Infrastructure Maintenance

Modern digital infrastructure generates heterogeneous, multimodal software artifacts encompassing structured code metrics, unstructured textual data such as commit logs and inline comments, and dynamic runtime signals whose complexity renders traditional defect detection approaches increasingly inadequate. This paper presents a cloud-native, transformer-based multimodal intelligence framework that integrates Large Language Model (LLM) semantic encoding with deep neural learning to enable automated defect prediction and proactive maintenance of large-scale digital infrastructure. The proposed system employs a sentence-transformer encoder (all-MiniLM-L6-v2) to process multimodal software artifact data—including serialized structured metrics and available textual fields—into dense 384-dimensional semantic embeddings. These embeddings are subsequently refined through a hierarchical multi-layer perceptron (MLP) deployed on a scalable cloud architecture for real-time inference. Evaluated on a real-world dataset of approximately 60,000 software modules, the framework achieves 99.72% accuracy, 100% precision, and an ROC-AUC of 0.9998, substantially outperforming baseline models including Random Forest, XGBoost, LSTM, and standalone MLP architectures. To address potential concerns regarding result validity, we conducted repeated experiments with five different random seeds (42, 0, 1, 7, 123) and ten-fold stratified cross-validation, confirming that performance metrics are stable across runs (accuracy: 0.9972 ± 0.0003). Data leakage was ruled out through strict temporal split ordering and pre-split SMOTE application exclusively on training folds. Confusion matrix and threshold-based analyses confirm strong classification performance with minimal false positives. SHAP-based explainability analysis further enhances the trustworthiness of the system by identifying the most influential multimodal predictors—past defect history, static analysis signals, and cyclomatic complexity—thereby contributing to transparent and accountable AI-driven infrastructure management. The presented framework advances the state of the art in LLM-driven multimodal systems by demonstrating how transformer intelligence, when applied to heterogeneous software artifact data streams, can enable reproducible, cloud-scalable, and interpretable maintenance pipelines for complex digital environments.

BERM: Low-Overhead Prompt-Injection Detection via In-Situ Benign Representation Modeling

BERM is introduced, a lightweight framework that performs in-situ detection by modeling a host LLM’s internal representations extracted during prefill, adding negligible overhead and reducing incremental inference overhead to near-zero.

LogPISA: An Improved Pre-Training and Tuning Pipeline for Log Understanding With Invariant and Semantic-Aware Objectives

With the rapid development of computer and network technology, network and software logs generated by a multitude of devices contain a wealth of knowledge and serve as a critical resource for intelligent fault diagnosis and efficient system operations. In recent years, various deep learning methods and the pre-training and fine-tuning paradigm of large language models have achieved significant success in log understanding. However, most existing approaches directly adapt models designed for natural language, overlooking the unique characteristics inherent to log data, such as its distinct vocabulary distribution, structural patterns, and semantic expressions. Concurrently, some template-based methods lack flexibility and are limited in their ability to mine deep semantic information. Therefore, we propose LogPISA, an improved pre-training and tuning pipeline for log understanding with invariant and semantic-aware objectives. Our framework employs a hybrid attention mechanism, combining standard self-attention with our novel Keyword-Aware Sparse Attention to enable the model to capture critical signals more efficiently. During the pre-training phase, we introduce two innovative self-supervised tasks: a non-contrastive learning task based on permutation invariance to capture the flexible ordering within log blocks, and a contrastive learning task based on log summarization to guide the model to focus on core semantics over superficial textual forms. Experimental results on several public benchmarks demonstrate that our model achieves excellent performance on downstream anomaly detection tasks. This validates that our proposed framework learns more robust and generalizable log representations, providing a high-quality representational foundation for various downstream log analysis tasks.

CodeBERT-SENet: Adaptive Syntax-Semantic Fusion via Gated Attention for Python Bug Detection and Localization

Real-Time Detection and Mitigation of Prompt Injection Attacks in LLM-Integrated Enterprise Systems

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.

LLM as Detector: An In-context Learning Approach for Tabular Anomaly Detection

LLM-Detector is proposed, a framework that utilizes the in-context learning capacity of LLMs for structured, prompt-conditioned scoring synthesis, enabling LLMs to derive anomaly detection logic from structured normal-state knowledge.