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
This study designs 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 into a single framework, and it is evaluated jointly along two axes: security and LLM performance.
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.
XiuYu Zhang, Bo-Nan 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
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