Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 1531-1538· 0 citations· 20 references
Abstract
Chatbots powered by LLMs have become popular due to their scalability and instant reply feature. There can be many security issues associated with LLM-based chatbots, which emerge in the interaction between the LLM-based model and other software components. One major problem is that chatbots can be attacked using prompt injection, jailbreaks, and harmful queries. These attacks can change the chatbot behavior and may lead to wrong answers or leakage of sensitive information. This makes security very important in such systems. Existing methods improve the chatbot performance using techniques like modular design, Retrieval-Augmented Generation (RAG) and input filtering for security. However, most of these methods use a single model and do not have strong real-time protection, making them weak against advanced attacks. Hence, this work proposes a Dual-LLM architecture where security is handled as a separate component for protection against real time threats. A classification model is designed to check the user input queries using threat categories and a confidence score is evaluated to block the harmful inputs. Only safe queries are sent to the response model, which uses a controlled RAG system to generate accurate answers. The system is built as a step-by-step pipeline combining classification and response generation. The results show an accuracy of 90% in detecting threats which leads to reduction of unsafe responses.
A prototype framework for an effective LLM API designed to mimic digital banking assistant responses and evaluate against synthesized and real-world banking dialogues is introduced and how the results dovetail with the growing regulatory landscape for AI in financial services is explored.
G. el-Tayeb, Abdalilah Alhalangy· The Scholar Journal for Scie...· 0 citations
Large Language Model (LLM)-based agents automate complex tasks by integrating external tools such as web browsers, e-mail clients, file readers, and APIs, but this same integration exposes them to indirect prompt injection (IPI) attacks, in which malicious instructions hidden in tool content hijack the agent. A central but often overlooked question is how defending against such attacks affects the LLM and its own task performance and computational efficiency. In this study, we design a comprehensive testbed and a layered defense, Spotlight-Guard, that combines spotlighting-based input isolation, an LLM detection-and-quarantine pipeline, and instruction integrity based on a Hash-based Message Authentication Code (HMAC) into a single framework, and we evaluate it jointly along two axes: security and LLM performance. Experiments on locally hosted 7B-class open-weight models (Qwen-2.5-7B, Mistral-7B, and DeepSeek-Coder) use Attack Success Rate (ASR) for security and benign-task success rate together with confusion-matrix-based metrics (precision, recall, and F1) for task performance, all with bootstrap 95% confidence intervals. Across a stratified, fixed-seed benchmark of 250 adversarial and 250 benign cases per configuration, the full system reduces the ASR from 36.0% to 17.2% while preserving a 97.2% benign-task success rate and raising the detection F1 from 0.749 to 0.892, demonstrating that strong protection need not degrade the model’s task performance. A component ablation isolates each layer’s contribution, an adaptive-attack evaluation confirms a low ASR (6.7%) under attacks crafted to target the pipeline, and an analysis of computational cost (model invocations per request) quantifies the efficiency overhead, characterizing the security–performance trade-off of layered defenses on open-weight LLMs.
Doygun Demirol, Murat Aydoğan· Applied Sciences· 0 citations
This work presents Language Model Security Modules (LMSM), a security framework that adapts the separation behind Linux Security Modules (LSM) to LLM serving and gives advances in interpretability and model-internal analysis a common path to runtime enforcement.
Xiucheng Zhang, Bonan Ruan, Junfeng Fang et al.· 0 citations
This thesis proposes a unified two-layer approach to enhancing the trustworthiness and security of LLM-enabled systems and introduces the Adaptive LLM Threat Response (ALTR) framework, which integrates behavioral anomaly detection, context-aware prompt classification, and temporal threat memory to identify and mitigate adversarial interactions in real time.
A hybrid browser-resident phishing detection framework that combines three complementary detection mechanisms: a locally executed Random Forest model using URL lexical features, lightweight real-time DOM structure analysis, and VirusTotal’s multi-engine reputation service is proposed.
Muhammad Arshad, Beena Sherin Kuriakose, C. W. Onn et al.· Frontiers of Computer Scienc...· 0 citations
End-to-end encrypted (E2EE) messaging apps are widely praised for their security and thus also used for sensitive coordination in group chats (e.g., by political decision makers). After Threema and WhatsApp, also Signal and iMessage have recently introduced polls to aid agreement processes in groups. This implicitly sets the expectation that all participants see the same outcome and thus have the same view of the conversation. This property is commonly referred to as transcript consistency (TC). In this work, we demonstrate that today's major E2EE messengers do not guarantee any form of TC for group chats, allowing a malicious group member to selectively omit, reorder, or present altered content to different recipients without triggering warnings in their user interface. We systematically investigate the extent of the problem under a malicious-participant threat model that targets the integrity of the shared transcript, or inconsistent delivery across a user's linked devices. We identify multiple equivocation vectors that range from protocol fallback paths to deliberate use of pairwise delivery channels within groups. We demonstrate concrete exploitation scenarios such as social engineering, evading moderation, and, in particular, rigging polls. Beyond these cross-service design issues, we also uncover implementation-specific behaviors with privacy implications (e.g., device OS fingerprinting). Finally, we contextualize our findings within prior transcript-consistency research and outline practical low-overhead mitigations and UI signaling strategies that can be integrated into state-of-the-art E2EE group protocols.
G. Gegenhuber, Moritz Grefner, Maximilian Günther et al.· 0 citations
