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20212026
most citedWho Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators

26 citations · 116 across the 9 of their papers we have counts for

collaborators

9 papers

cs.SE2026

Characterizing Software Aging in GPU-Based LLM Serving Systems

Domenico Cotroneo, Bojan Cukic

This paper proposes an empirical methodology to study software aging in GPU-based LLM serving systems. Traditional aging studies focus on CPU-centric software with relatively regul…

cs.SE2024

Enhancing AI-based Generation of Software Exploits with Contextual Information

Pietro Liguori, Cristina Improta, Roberto Natella +2

This practical experience report explores Neural Machine Translation (NMT) models' capability to generate offensive security code from natural language (NL) descriptions, highlight…

cs.CR2024★ 15 cited

AI Code Generators for Security: Friend or Foe?

Roberto Natella, Pietro Liguori, Cristina Improta +2

Recent advances of artificial intelligence (AI) code generators are opening new opportunities in software security research, including misuse by malicious actors. We review use cas…

cs.LG2023★ 7 cited

Enhancing Robustness of AI Offensive Code Generators via Data Augmentation

Cristina Improta, Pietro Liguori, Roberto Natella +2

Since manually writing software exploits for offensive security is time-consuming and requires expert knowledge, AI-base code generators are an attractive solution to enhance secur…

cs.SE2022★ 26 cited

Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators

Pietro Liguori, Cristina Improta, Roberto Natella +2

AI-based code generators are an emerging solution for automatically writing programs starting from descriptions in natural language, by using deep neural networks (Neural Machine T…

cs.CL2022★ 4 cited

Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation

Pietro Liguori, Cristina Improta, Simona De Vivo +3

Neural Machine Translation (NMT) has reached a level of maturity to be recognized as the premier method for the translation between different languages and aroused interest in diff…

cs.SE2022★ 23 cited

Can We Generate Shellcodes via Natural Language? An Empirical Study

Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo +3

Writing software exploits is an important practice for offensive security analysts to investigate and prevent attacks. In particular, shellcodes are especially time-consuming and a…

cs.SE2021★ 26 cited

EVIL: Exploiting Software via Natural Language

Pietro Liguori, Erfan Al-Hossami, Vittorio Orbinato +4

Writing exploits for security assessment is a challenging task. The writer needs to master programming and obfuscation techniques to develop a successful exploit. To make the task…

cs.SE2021★ 15 cited

Shellcode_IA32: A Dataset for Automatic Shellcode Generation

Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo +3

We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, sta…