Percorrer por autor "PEREIRA, PEDRO EMANUEL SOUSA"
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- Securing retrieval-augmented generationPublication . PEREIRA, PEDRO EMANUEL SOUSA; Pereira, Isabel Cecília Correia da Silva Praça Gomes; Maia, Eva Catarina GomesRetrieval-Augmented Generation (RAG) systems improve the factual grounding of language models by retrieving external documents before generating an answer. However, this dependence on external knowledge also creates a security risk, if the retrieval corpus is poisoned, the generated response may become incorrect while still appearing evidence-based. This thesis investigates the robustness of RAG pipelines against knowledge-base poisoning attacks. It rst analyzes how retrieval architecture, retrieval depth, database composition, chunking, dataset characteristics, and generator choice in uence poisoning vulnerability. The results show that robustness is a pipeline-level property, dense and graph-based retrieval are generally more resistant than lexical retrieval, but larger top-K values and poisoned multi-database settings increase exposure to adversarial content. The thesis then introduces Micro Collaborative Poisoning, a distributed attack in which several small, plausible poisoned documents collectively support the same false claim. Experiments show that this attack is less obvious at the document level than stronger concentrated poisoning, yet it can still achieve comparable downstream attack success. Overall, the ndings demonstrate that trustworthy RAG systems require defenses that combine robust retrieval, source integrity, cross-document analysis, and cautious generation.