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Documents authored by Lupu, Emil

Document
DOI: 10.4230/DagRep.15.11.87
Security and Privacy of Large Language Models (Dagstuhl Seminar 25461)

Authors: Stephan Günnemann, Pavel Laskov, Emil Lupu, Vera Rimmer, Advije Rizvani, and Qianying Liao

Published in: Dagstuhl Reports, Volume 15, Issue 11 (2026)

Abstract
Large Language Models (LLMs) have rapidly evolved from experimental systems capable of generating simple text to powerful general-purpose tools that solve exam-level problems, assist human experts, summarize complex documents, and write code, leading to their widespread deployment in production environments to improve productivity. Their rapid adoption has outpaced scientific understanding of the associated security and privacy risks, creating a growing gap between real-world use and trust in LLM-based applications. Unlike traditional machine learning models, LLMs are inherently general-purpose, operate at unprecedented scale, are often proprietary, and are accessed through interactive dialog interfaces. This makes their behavior difficult to predict and introduces novel attack surfaces and emerging phenomena such as hallucinations. Existing methods and analyzes for conventional machine learning are insufficient to cope with these aspects. So, this Dagstuhl Seminar "Security and Privacy of Large Language Models" (25461) aimed to initiate a systematic scientific discussion on the security and privacy of language models by bringing together researchers from AI, security, privacy, and natural language processing to address three central questions: how safe and secure are LLMs in adversarial environments; to what extent users’ privacy may be at risk when interacting with LLMs; and what other categories of societal impact may arise from the rapid advancement of LLM technologies. The seminar sought to systematize existing knowledge, identify high-risk applications and prevalent attack vectors, discuss defense strategies and deployment challenges, and outline directions for future research in LLM security and privacy.
Cite as

Stephan Günnemann, Pavel Laskov, Emil Lupu, Vera Rimmer, Advije Rizvani, and Qianying Liao. Security and Privacy of Large Language Models (Dagstuhl Seminar 25461). In Dagstuhl Reports, Volume 15, Issue 11, pp. 87-113, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{gunnemann_et_al:DagRep.15.11.87,
  author =	{G\"{u}nnemann, Stephan and Laskov, Pavel and Lupu, Emil and Rimmer, Vera and Rizvani, Advije and Liao, Qianying},
  title =	{{Security and Privacy of Large Language Models (Dagstuhl Seminar 25461)}},
  pages =	{87--113},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2026},
  volume =	{15},
  number =	{11},
  editor =	{G\"{u}nnemann, Stephan and Laskov, Pavel and Lupu, Emil and Rimmer, Vera and Rizvani, Advije and Liao, Qianying},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.11.87},
  URN =		{urn:nbn:de:0030-drops-274951},
  doi =		{10.4230/DagRep.15.11.87},
  annote =	{Keywords: Large Language Models, Artifician Intelligence, Security and Privacy}
}
Document
DOI: 10.4230/LIPIcs.ICLP.2011.175
Multi-agent Confidential Abductive Reasoning

Authors: Jiefei Ma, Alessandra Russo, Krysia Broda, and Emil Lupu

Published in: LIPIcs, Volume 11, Technical Communications of the 27th International Conference on Logic Programming (ICLP'11) (2011)

Abstract
In the context of multi-agent hypothetical reasoning, agents typically have partial knowledge about their environments, and the union of such knowledge is still incomplete to represent the whole world. Thus, given a global query they collaborate with each other to make correct inferences and hypothesis, whilst maintaining global constraints. Most collaborative reasoning systems operate on the assumption that agents can share or communicate any information they have. However, in application domains like multi-agent systems for healthcare or distributed software agents for security policies in coalition networks, confidentiality of knowledge is an additional primary concern. These agents are required to collaborately compute consistent answers for a query whilst preserving their own private information. This paper addresses this issue showing how this dichotomy between "open communication" in collaborative reasoning and protection of confidentiality can be accommodated. We present a general-purpose distributed abductive logic programming system for multi-agent hypothetical reasoning with confidentiality. Specifically, the system computes consistent conditional answers for a query over a set of distributed normal logic programs with possibly unbound domains and arithmetic constraints, preserving the private information within the logic programs. A case study on security policy analysis in distributed coalition networks is described, as an example of many applications of this system.
Cite as

Jiefei Ma, Alessandra Russo, Krysia Broda, and Emil Lupu. Multi-agent Confidential Abductive Reasoning. In Technical Communications of the 27th International Conference on Logic Programming (ICLP'11). Leibniz International Proceedings in Informatics (LIPIcs), Volume 11, pp. 175-186, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{ma_et_al:LIPIcs.ICLP.2011.175,
  author =	{Ma, Jiefei and Russo, Alessandra and Broda, Krysia and Lupu, Emil},
  title =	{{Multi-agent Confidential Abductive Reasoning}},
  booktitle =	{Technical Communications of the 27th International Conference on Logic Programming (ICLP'11)},
  pages =	{175--186},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-31-6},
  ISSN =	{1868-8969},
  year =	{2011},
  volume =	{11},
  editor =	{Gallagher, John P. and Gelfond, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2011.175},
  URN =		{urn:nbn:de:0030-drops-31736},
  doi =		{10.4230/LIPIcs.ICLP.2011.175},
  annote =	{Keywords: Abductive Logic Programming, Coordination, Agents}
}
Document
DOI: 10.4230/LIPIcs.ICLP.2010.54
Inductive Logic Programming as Abductive Search

Authors: Domenico Corapi, Alessandra Russo, and Emil Lupu

Published in: LIPIcs, Volume 7, Technical Communications of the 26th International Conference on Logic Programming (2010)

Abstract
We present a novel approach to non-monotonic ILP and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ILP systems based on Inverse Entailment and is the first top-down ILP system that allows background theories and hypotheses to be normal logic programs. The approach relies on mapping an ILP problem into an equivalent ALP one. This enables the use of established ALP proof procedures and the specification of richer language bias with integrity constraints. The mapping provides a principled search space for an ILP problem, over which an abductive search is used to compute inductive solutions.
Cite as

Domenico Corapi, Alessandra Russo, and Emil Lupu. Inductive Logic Programming as Abductive Search. In Technical Communications of the 26th International Conference on Logic Programming. Leibniz International Proceedings in Informatics (LIPIcs), Volume 7, pp. 54-63, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{corapi_et_al:LIPIcs.ICLP.2010.54,
  author =	{Corapi, Domenico and Russo, Alessandra and Lupu, Emil},
  title =	{{Inductive Logic Programming as Abductive Search}},
  booktitle =	{Technical Communications of the 26th International Conference on Logic Programming},
  pages =	{54--63},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-17-0},
  ISSN =	{1868-8969},
  year =	{2010},
  volume =	{7},
  editor =	{Hermenegildo, Manuel and Schaub, Torsten},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2010.54},
  URN =		{urn:nbn:de:0030-drops-25838},
  doi =		{10.4230/LIPIcs.ICLP.2010.54},
  annote =	{Keywords: Inductive Logic Programming, Abductive Logic Programming, Non-monotonic Reasoning}
}
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