activity
20202025
most citedFundamentals of Generative Large Language Models and Perspectives in Cyber-Defense

23 citations · 29 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CL2025

From Model to Breach: Towards Actionable LLM-Generated Vulnerabilities Reporting

Cyril Vallez, Alexander Sternfeld, Andrei Kucharavy +1

As the role of Large Language Models (LLM)-based coding assistants in software development becomes more critical, so does the role of the bugs they generate in the overall cybersec…

cs.CL2025

TypePilot: Leveraging the Scala Type System for Secure LLM-generated Code

Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic

Large language Models (LLMs) have shown remarkable proficiency in code generation tasks across various programming languages. However, their outputs often contain subtle but critic…

cs.CL2025

Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs

Alexander Sternfeld, Andrei Kucharavy, Dimitri Percia David +3

Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Communication Technologies (ICTs). Tr…

cs.CL2024

LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts

Henrique Da Silva Gameiro, Andrei Kucharavy, Ljiljana Dolamic

With the emergence of widely available powerful LLMs, disinformation generated by large Language Models (LLMs) has become a major concern. Historically, LLM detectors have been tou…

cs.CL2023★ 1 cited

LLMs Perform Poorly at Concept Extraction in Cyber-security Research Literature

Maxime Würsch, Andrei Kucharavy, Dimitri Percia David +1

The cybersecurity landscape evolves rapidly and poses threats to organizations. To enhance resilience, one needs to track the latest developments and trends in the domain. It has b…

cs.NE2023

Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning

Andrei Kucharavy, Rachid Guerraoui, Ljiljana Dolamic

Whenever applicable, the Stochastic Gradient Descent (SGD) has shown itself to be unreasonably effective. Instead of underperforming and getting trapped in local minima due to the…