2 citations · 8 across the 19 of their papers we have counts for
10 papers · 1 filter
Towards LLM-Enhanced Android Taint Analysis
Nicholas Miazzo, Marco Alecci, Jordan Samhi +2
Taint analysis is a fundamental technique for detecting sensitive data leaks in Android apps. However, traditional static tools, such as FlowDroid, still face well-known challenges…
Evaluating LLMs for Obfuscation Detection and Classification in Android Apps
Luca Ferrari, Marco Alecci, Jordan Samhi +4
Android applications (apps) developers increasingly rely on code obfuscation techniques to hinder reverse engineering and protect intellectual property. However, obfuscation also r…
Learned or Memorized ? Quantifying Memorization Advantage in Code LLMs
Djiré Albérick Euraste, Kaboré Abdoul Kader, Jordan Samhi +3
The lack of transparency about code datasets used to train large language models (LLMs) makes it difficult to detect, evaluate, and mitigate data leakage. We present a perturbation…
From Rookie to Pro: Social Engineering LLMs for Automated Vulnerability Exploitation in Enterprise Software
Moustapha Awwalou Diouf, Maimouna Tamah Diao, Iyiola Emmanuel Olatunji +6
LLMs democratize software engineering by enabling non-programmers to create applications, but this same accessibility fundamentally undermines security assumptions that have guided…
Exploring Hidden Geographic Disparities in Android Apps
M. Alecci, P. Jiménez, J. Samhi +2
While mobile app evolution has been widely studied, geographical variation in app behavior remains largely unexplored. This paper presents a large-scale study of location-based And…
GAPS: Targeted Execution of Android Apps via Static Path Reconstruction
Samuele Doria, Alexander Pilgun, Jordan Samhi +2
Targeted execution of Android applications (apps) remains a longstanding challenge for software testing and analysis. Although recent advances in GUI testing have substantially imp…
SIEVE: Towards Verifiable Certification for Code-datasets
Fatou Ndiaye Mbodji, El-hacen Diallo, Jordan Samhi +3
Code agents and empirical software engineering rely on public code datasets, yet these datasets lack verifiable quality guarantees. Static 'dataset cards' inform, but they are neit…
Multi-Agent Strategies for Bridging Programming Language Gaps in Code Generation
Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Albérick Euraste Djire +3
Real-world software systems are inherently multilingual, but current large language models are not equally consistent across programming languages. This mismatch limits code genera…
The Code Barrier: What LLMs Actually Understand?
Serge Lionel Nikiema, Jordan Samhi, Abdoul Kader Kaboré +2
Understanding code represents a core ability needed for automating software development tasks. While foundation models like LLMs show impressive results across many software engine…
Call Graph Soundness in Android Static Analysis
Jordan Samhi, René Just, Tegawendé F. Bissyandé +2
Static analysis is sound in theory, but an implementation may unsoundly fail to analyze all of a program's code. Any such omission is a serious threat to the validity of the tool's…