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.
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
A layered hybrid defense gateway that combines three complementary detectors: a fast prefilter based on lexical rules, a semantic classifier that uses word-level TF-IDF features together with a multilayer perceptron, and a character-level classifier that is robust to obfuscation is proposed.
O. Esanov, Ch. Erkinova· 2022-yil 3-son (133/1) ANIQ...· 0 citations
DSPrompt is proposed, a Dynamic Soft Prompt defense framework that directly reshapes the retriever's embedding semantics, without modifying the retrieval pipeline, and is consistently outperforming existing defense baselines at a fraction of their computational cost.
Chang Liu, Y. Lai, Mingyue Cui et al.· 0 citations
Experiments show that BASIS maintains near-perfect injection detection while substantially reducing over-refusal on safe attack samples, especially under robust instruction templates.
Laiqiao Qin, Tianqing Zhu, Longxiang Gao et al.· 0 citations
DeCNIP (Defense with Critical Neuron Isolation Pruning), which leverages representational analysis to identify and neutralize backdoors in a unified pipeline, is introduced, which achieves over 95% relative reduction in Attack Success Rate (ASR), outperforming seven state-of-the-art defenses with only 0.1% neuron intervention.
Yuxi Li, Zhibo Zhang, Kailong Wang et al.· 0 citations
Modern Software-as-a-Service (SaaS) environments generate voluminous, heterogeneous logs that exhibit rapid schema drift and semantic sparsity, rendering traditional template-based detection methods ineffective. This paper presents LLM-EP, a unified framework that utilizes Large Language Models (LLMs) for proactive error prediction via semantic log analysis. Unlike existing heuristics, LLM-EP employs a drift resilient semantic parser and an embedding driven normalization engine to maintain accuracy amidst evolving log formats. We propose a hybrid offline-online architecture that balances deep semantic understanding with real-time inference requirements, incorporating a robust outof-vocabulary (OOV) mechanism for zero-shot generalization to unseen log patterns. Evaluation on production SaaS datasets indicates that LLM-EP achieves a 25% gain in parsing accuracy and a 12% improvement in F1-score over state-of-the-art baselines. Most notably, LLM-EP reduces Mean Time to Detect (MTTD) by up to 60%, demonstrating its efficacy as a scalable foundation for automated site reliability engineering.
