works on

From the 1 of 19 linked papers with an AI index.

activity
20242026
collaborators

19 papers

cs.SE2026

When Knowledge Changes: Metamorphic Testing of RAG Systems with Mutations

Jinhan Kim, Samuele Pasini, Paolo Tonella

The paper proposes a metamorphic testing framework that assesses how Retrieval‑Augmented Generation (RAG) systems behave when their underlying document corpora change, using a taxo…

cs.SE2026

Testing Retrieval-Augmented Generation Systems with Chunk Coverage

Jinhan Kim, Samuele Pasini, Paolo Tonella

Retrieval-Augmented Generation (RAG)-based systems\footnote{For brevity, RAG-based systems are referred to as RAG systems throughout this paper.} are increasingly deployed in high-…

cs.CR2026

Detecting Trojaned DNNs via Spectral Regression Analysis

Samuele Pasini, Jinhan Kim, Paolo Tonella

Modern DNNs are repeatedly fine-tuned to incorporate new data and functionality. This evolutionary workflow introduces a security risk when updated data cannot be fully trusted, as…

cs.LG2026

TopoMap: A Feature-based Semantic Discriminator of the Topographical Regions in the Test Input Space

Gianmarco De Vita, Nargiz Humbatova, Paolo Tonella

Testing Deep Learning (DL)-based systems is an open challenge. Although it is relatively easy to find inputs that cause a DL model to misbehave, the grouping of inputs by features…

cs.SE2026

Cross-site scripting adversarial attacks based on deep reinforcement learning: Evaluation and extension study

Samuele Pasini, Gianluca Maragliano, Jinhan Kim +1

Cross-site scripting (XSS) poses a significant threat to web application security. While Deep Learning (DL) has shown remarkable success in detecting XSS attacks, it remains vulner…

cs.CR2026

Dynamic Deception: When Pedestrians Team Up to Fool Autonomous Cars

Masoud Jamshidiyan Tehrani, Marco Gabriel, Jinhan Kim +1

Many adversarial attacks on autonomous-driving perception models fail to cause system-level failures once deployed in a full driving stack. The main reason for such ineffectiveness…