Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
10.0
4.6
Journals
Future Internet
Special Issues
Advances in Agentic and Generative AI for Secure Software Systems...
share announcement

Advances in Agentic and Generative AI for Secure Software Systems and Cyber-Resilience in Future Internet

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: 25 December 2026 | Viewed by 1490

Editors

Dr. Ajay Bandi

E-Mail Website
Guest Editor
School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA
Interests: generative AI; cybersecurity; secure software systems; spatial computing
Special Issues, Collections and Topics in MDPI journals
Dr. Zhengrui Qin

E-Mail Website
Guest Editor
School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA
Interests: cybersecurity; cyber-physical systems; mobile computing

Special Issue Information

Dear Colleagues,

Generative AI is quickly becoming an essential element in all the different phases of the software development life cycle. Agentic and generative AI applications are capable of secure code generation, vulnerability detection, automated testing and adaptive defenses. At the same time, there are new vulnerabilities to exploit the next-gen resilience of digital ecosystems. Simultaneously, the growing AI adversaries and the increasing complexity of cyber-attacks demand intelligent, resilient and proactive solutions to safeguard the critical infrastructures and trust in the future internet.

The Special Issue, titled Advances in Agentic and Generative AI for Secure Software Systems and Cyber Resilience in the Future Internet, aims to solicit the contributions of the application of agentic and generative AI technologies in secure software systems and cybersecurity. We invite submissions of high-quality original research articles, surveys and systematic literature reviews covering the topics associated with AI practices in cybersecurity and software engineering. By bringing together researchers and practitioners from industry, this Special Issue aims to advance knowledge and foster innovative solutions that strengthen cyber-resilience and shape trustworthy and secure systems for the future internet.

Special Issue Topics:

The topics include, but are not limited to, the following:

  • Agentic AI in software development
  • Agentic AI in software engineering
  • Generative AI for secure coding and system design
  • AI-driven threat modeling and simulation
  • Adversarial attack detection and defense
  • Design of AI agents for software and security applications
  • AI agents for secure coding and system design
  • AI agents for automated testing
  • AI agents for adaptive defense
  • AI agents for vulnerability detection
  • AI agents for cyber-resilience in the Future Internet

Dr. Ajay Bandi
Dr. Zhengrui Qin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agentic AI
  • generative AI
  • secure software development
  • cyber-resilience
  • AI-driven threat modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

52 pages, 8425 KB  
Article
PromptSentinel-X: A Leakage-Aware and Context-Aware Framework for Prompt-Injection Detection in Large Language Model-Powered Web Agents
by Lily Popova Zhuhadar
Future Internet 2026, 18(7), 376; https://doi.org/10.3390/fi18070376 - 19 Jul 2026
Viewed by 638
Abstract
Large language model (LLM)-powered web agents combine privileged instructions with user requests, webpages, retrieved documents, tool outputs, memory, and conversation history, creating prompt-injection risks that static text classification may not capture. This study presents PromptSentinel-X, a leakage-aware and context-aware screening framework. The primary [...] Read more.
Large language model (LLM)-powered web agents combine privileged instructions with user requests, webpages, retrieved documents, tool outputs, memory, and conversation history, creating prompt-injection risks that static text classification may not capture. This study presents PromptSentinel-X, a leakage-aware and context-aware screening framework. The primary benchmark contained 1581 English records from the Prompt Injection Malignant dataset; 30,015 external prompt-injection, jailbreak, and benign hard-negative records were reserved for transfer and stress testing. The framework uses prompt-family-aware partitioning, trusted–untrusted context segmentation, calibrated risk prediction, robustness analysis, and deployment-oriented routing. On a 465-record group-aware test set, PromptSentinel-X achieved 0.9849 accuracy, 0.8887 macro-F1, 0.9851 attack recall, a 0.0050 benign false-positive rate, 0.9971 area under the receiver operating characteristic curve (AUROC), 0.9836 area under the precision-recall curve (AUPRC), and 0.0153 expected calibration error. Random splitting produced a higher baseline macro-F1 but 144 leakage warnings. Context-aware macro-F1 declined from 0.8301 for static prompts to 0.6306 for multi-turn, 0.3844 for retrieval-augmented generation, and 0.3200 for memory scenarios. PromptSentinel-X is positioned as a calibrated screening and escalation component, not a stand-alone authorization mechanism. Broader tool, memory, multimodal browser, multilingual, and end-to-end agent studies remain necessary. Full article
(This article belongs to the Special Issue Advances in Agentic and Generative AI for Secure Software Systems and Cyber-Resilience in Future Internet)
►▼ Show Figures

Figure 1

22 pages, 2352 KB  
Article
Phase-Adaptive Model Routing in LLM-Driven SSH Honeypots: Balancing Response Fidelity and Latency Across the Attack Lifecycle
by Raiymbek Magazov, Fatima Uralova, Kuanysh Abeshev, Guldana Akhmedi and Gulnur Aksholak
Future Internet 2026, 18(7), 359; https://doi.org/10.3390/fi18070359 - 14 Jul 2026
Viewed by 427
Abstract
Secure Shell (SSH) intrusions against Linux servers remain a dominant vector of opportunistic and targeted cyber incidents, yet operational honeypots treat attacker commands either as isolated lookup keys for static templates or as input to a single uniform language model. Two research directions [...] Read more.
Secure Shell (SSH) intrusions against Linux servers remain a dominant vector of opportunistic and targeted cyber incidents, yet operational honeypots treat attacker commands either as isolated lookup keys for static templates or as input to a single uniform language model. Two research directions partially address this gap: LLM-driven response generation raises interaction realism but incurs a fidelity–latency trade-off, while semantic command analytics classifies attack stages from embeddings yet applies a single fine-tuned model uniformly across sessions. This work introduces the phase-adaptive model routing framework (PAMR), which explicitly couples both views. A lightweight online phase estimator infers the current attack stage—reconnaissance, exploitation, or persistence—from a sliding window of recent commands via compact semantic embeddings; a router then dispatches each command to one of several heterogeneous LLM backends according to estimated phase, command complexity, and a confidence-weighted latency budget. The routing decision is formulated as a constrained optimization problem and realized at runtime as an O1 lookup in a precomputed dispatch table. PAMR is evaluated on a controlled, reproducible benchmark of 412 stage-annotated command sessions that combines representative Linux command–response pairs with synthesized attacker traces; we explicitly state that this is a laboratory benchmark rather than live attacker traffic, and we scope our claims accordingly. Relative to uniform-model baselines, PAMR reduces mean response latency by approximately 38% against an API-hosted high-capacity backend and by approximately 45% against a locally hosted mid-capacity backend, while keeping token-level response-fidelity metrics close to the high-capacity baseline (cosine similarity ≈ 0.39 vs. 0.40) and maintaining an online stage-classification macro-F1 above 0.87. We further provide a first-order analytical treatment of the timing side-channel that any backend-routing architecture introduces, and we frame the contribution as a latency/token-fidelity trade-off, leaving validation of operational realism against live adversaries to future work. Full article
(This article belongs to the Special Issue Advances in Agentic and Generative AI for Secure Software Systems and Cyber-Resilience in Future Internet)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop