Papers by Giuseppe Siracusano
AutoPenBench: A Vulnerability Testing Benchmark for Generative Agents (2025.emnlp-industry)
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Luca Gioacchini, Alexander Delsanto, Idilio Drago, Marco Mellia, Giuseppe Siracusano, Roberto Bifulco
| Challenge: | LLM agents are promising for vulnerability testing, but lack benchmarks to evaluate and compare them. |
| Approach: | They propose an open-source benchmark for the evaluation of vulnerability testing agents that includes 33 tasks ranging from introductory exercises to actual vulnerable systems. |
| Outcome: | The proposed benchmark includes 33 tasks ranging from introductory exercises to actual vulnerable systems. |
AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents (2024.naacl-demo)
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Luca Gioacchini, Giuseppe Siracusano, Davide Sanvito, Kiril Gashteovski, David Friede, Roberto Bifulco, Carolin Lawrence
| Challenge: | Existing benchmarks are narrow and simply compute overall task success. |
| Approach: | They propose a framework where both benchmarks and metrics are modular and easily extensible through well documented and easy-to-use APIs. |
| Outcome: | The proposed framework can track agent progress on two use cases and identify common failure points and refine the agent architecture to obtain a significant performance increase. |
What Did I Do Wrong? Quantifying LLMs’ Sensitivity and Consistency to Prompt Engineering (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have significantly improved productivity in a number of routine tasks. |
| Approach: | They propose two metrics for classification tasks, namely *sensitivity* and *consistency*, which are complementary to task performance. |
| Outcome: | The proposed metrics are complementary to task performance and can be used to guide prompt engineering and obtain LLMs that balance robustness and performance. |
AutoCVSS: Assessing the Performance of LLMs for Automated Software Vulnerability Scoring (2025.emnlp-industry)
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| Challenge: | Increasing number of daily disclosed software vulnerabilities imposes significant pressure on security analysts, extending the time between disclosure and exploitation. |
| Approach: | They propose to use Large Language Models to automate vulnerability risk score prediction using the industrial CVSS standard. |
| Outcome: | The proposed model complements baselines in data-scarce settings without annotated data, highlighting their value in improving vulnerability management. |