33 citations · 37 across the 7 of their papers we have counts for
8 papers
Dense Contexts Are Hard Contexts: Lexical Density Limits Effective Context in LLMs
Giovanni Dettori, Matteo Boffa, Danilo Giordano +2
Input length and the position of relevant information are widely cited as the primary causes of degraded LLM long-context performance. Here, we study lexical density -- the rate at…
Autonomous LLM Agents & CTFs: A Second Look
Youness Bouchari, Matteo Boffa, Marco Mellia +3
Large Language Model (LLM) agents are increasingly proposed to automate offensive security tasks, with recent studies reporting near human-level success rates in Capture-the-Flag (…
Improving Generalization on Cybersecurity Tasks with Multi-Modal Contrastive Learning
Jianan Huang, Rodolfo V. Valentim, Luca Vassio +4
The use of ML in cybersecurity has long been impaired by generalization issues: Models that work well in controlled scenarios fail to maintain performance in production. The root c…
Towards Agentic Honeynet Configuration
Federico Mirra, Matteo Boffa, Idilio Drago +2
Honeypots are deception systems that emulate vulnerable services to collect threat intelligence. While deploying many honeypots increases the opportunity to observe attacker behavi…
Large-Scale Constraint Generation -- Can LLMs Parse Hundreds of Constraints?
Matteo Boffa, Jiaxuan You
Recent research has explored the constrained generation capabilities of Large Language Models (LLMs) when explicitly prompted by few task-specific requirements. In contrast, we int…
CyberSleuth: Autonomous Blue-Team LLM Agent for Web Attack Forensics
Stefano Fumero, Kai Huang, Matteo Boffa +3
Post-mortem analysis of compromised systems is a key aspect of cyber forensics, today a mostly manual, slow, and error-prone task. Agentic AI, i.e., LLM-powered agents, is a promis…
LogPrécis: Unleashing Language Models for Automated Malicious Log Analysis
Matteo Boffa, Rodolfo Vieira Valentim, Luca Vassio +4
The collection of security-related logs holds the key to understanding attack behaviors and diagnosing vulnerabilities. Still, their analysis remains a daunting challenge. Recently…
Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure
Matteo Boffa, Zied Ben Houidi, Jonatan Krolikowski +1
Recent years have witnessed the promise that reinforcement learning, coupled with Graph Neural Network (GNN) architectures, could learn to solve hard combinatorial optimization pro…