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20242026
most citedReasoning Beyond Limits: Advances and Open Problems for LLMs

19 citations · 19 across the 12 of their papers we have counts for

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11 papers · 1 filter

cs.CR2025

From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows

Mohamed Amine Ferrag, Norbert Tihanyi, Djallel Hamouda +3

Autonomous AI agents powered by large language models (LLMs) with structured function-calling interfaces enable real-time data retrieval, computation, and multi-step orchestration.…

cs.CR2025

The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution

Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky +2

In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which model produced it, enabling relia…

cs.AI2025

UAVBench: An Open Benchmark Dataset for Autonomous and Agentic AI UAV Systems via LLM-Generated Flight Scenarios

Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah

Autonomous aerial systems increasingly rely on large language models (LLMs) for mission planning, perception, and decision-making, yet the lack of standardized and physically groun…

cs.CR2025

Reliability and Resilience of AI-Driven Critical Network Infrastructure under Cyber-Physical Threats

Konstantinos A. Lizos, Leandros Maglaras, Elena Petrovik +3

The increasing reliance on AI-driven 5G/6G network infrastructures for mission-critical services highlights the need for reliability and resilience against sophisticated cyber-phys…

cs.CR2025

Innovating Augmented Reality Security: Recent E2E Encryption Approaches

Hamish Alsop, Leandros Maglaras, Helge Janicke +2

End-to-end encryption (E2EE) has emerged as a fundamental element of modern digital communication, protecting data from unauthorized access during transmission. By design, E2EE ens…

cs.LG2025

I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution

Tamas Bisztray, Bilel Cherif, Richard A. Dubniczky +6

Detecting AI-generated code, deepfakes, and other synthetic content is an emerging research challenge. As code generated by Large Language Models (LLMs) becomes more common, identi…