Jul 2026· 2022-yil 3-son (133/1) ANIQ FANLAR SERIYASI· Vol 2, pp. 207-216· 0 citations
TL;DR
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.
Abstract
Large language models (LLMs) are now embedded in many digital systems, which has exposed a new and distinctive attack surface. The most pressing threat is the prompt injection attack, in which adversarial text causes a model to ignore its developer-defined instructions, leak its hidden system prompt, or perform unintended actions. This paper proposes 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. Their outputs are merged by a lightweight meta-decision block based on logistic regression. In a fully reproducible experiment, the detectors are trained on clean and standard obfuscated attacks and evaluated on held-out data that additionally contains unseen, newly phrased attacks and an unseen adaptive obfuscation style. On this controlled benchmark the proposed hybrid system reaches an F1 score of 0.989 and the lowest false-positive rate among all compared systems, clearly outperforming a brittle rule-based baseline (F1 = 0.504) and every single classifier across all attack categories. An ablation study confirms that each layer covers a distinct class of attacks, empirically supporting the defense-in-depth principle.
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
Large Language Models (LLMs) are increasingly used to automate high-stakes screening decisions, yet they can be manipulated by adversarial instructions hidden in the documents they evaluate. This paper introduces a benchmark for this vulnerability in LLM-based resume screening: 463 job-candidate pairs drawn from a 14-domain corpus, with the evaluated sample covering 13 domains, attacked through a taxonomy of four attack types and four injection positions (16 attack configurations). Across 12 model configurations covering open-weight and proprietary models, some attack types exceed 80% attack success rate (ASR) when the injected content reaches the model, and attacks upgrade up to 73.4% of candidates unanimously rejected by human annotators. The hidden-content attacks assume the resume text or HTML reaches the model; an end-to-end parser-layer analysis shows that style-aware sanitization removes most of them before classification, whereas visible-text attacks survive. We evaluate prompt-based defenses on all 12 model configurations and our proposed FIDS (Foreign Instruction Detection through Separation), a fine-tuning defense, on Qwen3-8B, the one base model we could fine-tune. On Qwen3-8B, in paired configuration-level comparisons against a common no-defense baseline, prompt-based defense reduces ASR by 10.1 percentage points (pp; 95% bootstrap CI [6.3, 14.3]), FIDS by 15.4 pp (95% CI [8.8, 23.4]), and their combination by 26.3 pp (95% CI [18.2, 35.0]), at the cost of also downgrading candidates the undefended model had accepted (a proxy for false rejections), by 12.5, 10.4, and 19.4 pp respectively. No defense eliminates the attacks, training-time and prompt-only defenses have comparable utility costs, and whether these trade-offs carry over to the proprietary models we cannot fine-tune is left to future work.
Hong-Lin Mu, Jinghao Liu, Kaiyang Wan et al.· International Journal of Mac...· 3 citations· ⚡1
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.
A defense taxonomy spanning three axes, namely prompt-level, inference-time, and training-time interventions, is proposed, within which 30 mitigation mechanisms published from 2024 onwards are systematically analyzed, demonstrating that no single defense mechanism provides comprehensive protection, and that robust deployment mandates layered, complementary strategies.
Berkay Özçam, Mustafa Kara, Muhammet Ali Aydin et al.· Electronics· 0 citations
The increasing deployment of Large Language Models (LLMs) in critical infrastructure has introduced a class of security risks that remain insufficiently characterised and poorly tooled in practice. Attack vectors including prompt injection, jailbreaking, code execution facilitation, covert data exfiltration, and training data poisoning present quantifiable threats to systems that rely on LLM outputs, yet no lightweight, provider-agnostic tool exists to measure these risks systematically. This paper presents VectorSec, an open-source, web-based LLM security scanner built with Python and Dash. The tool executes a structured test suite of 320 adversarial prompts spanning 16 vulnerability categories aligned with the OWASP Top 10 for LLMs. Each response is scored through a four-stage pipeline combining pattern matching, sentiment analysis, semantic similarity, and secondary LLM verification. Evaluation across three open-source models demonstrates that pattern-matching-only filtering misses approximately 15% of High and Critical findings that semantic verification correctly identifies, motivating the multi-layer design. VectorSec provides interactive dashboard reporting, real-time progress tracking, and audit-ready PDF and CSV exports, lowering the barrier to structured LLM security assessment in operational settings.
M. Yamin· International Conference on...· 0 citations
The remarkable instruction-following ability of modern LLMs has enabled their practical use as the minds of agents that can autonomously complete increasingly complex tasks. Therein, however, also lies their vulnerability to attacks which embed malicious instructions in text, common variants of which are known as indirect prompt injection (IPI). A fundamental task in addressing this vulnerability is successful segmentation of a given text into benign and malicious sentences (if any). While a number of approaches for this task have been proposed, no detector combines query-relative detection at the segment level, and none are hardened against adaptive evasion attacks realizable in agentic executions. We address the former limitation by developing an approach for malicious sentence classification that is both context- and query-aware. Next, to harden the resulting classifier against evasion, we present two adversarial training methods. The first is directly adapted feature-space adversarial training (AT) in which evasions are approximated using projected-gradient-based optimization in the embedding space. The second simulates realizable evasion attacks in the AT loop through LLM-based paraphrasing. Crucially, we parametrize both AT variants to facilitate a smooth tradeoff between utility and attack robustness. In extensive experiments using indirect prompt injection benchmarks we show that the proposed approach outperforms state-of-the-art IPI defense baselines under static attacks, while in the case of adaptive attacks, our AT variants provide significantly higher utility, lower attack success rate, and often both. Finally, we show that the best AT parameters can depend intimately on the particular application domain. Consequently, domain-dependent tuning of malicious text detectors is likely necessary in practice. Our code is publicly available at https://github.com/tavia-liu/CAD.