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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.FORC.2025.9

Optimal Rates for Robust Stochastic Convex Optimization

Authors: Changyu Gao, Andrew Lowy, Xingyu Zhou, and Stephen J. Wright

Published in: LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025)

Abstract

Machine learning algorithms in high-dimensional settings are highly susceptible to the influence of even a small fraction of structured outliers, making robust optimization techniques essential. In particular, within the ε-contamination model, where an adversary can inspect and replace up to an ε-fraction of the samples, a fundamental open problem is determining the optimal rates for robust stochastic convex optimization (SCO) under such contamination. We develop novel algorithms that achieve minimax-optimal excess risk (up to logarithmic factors) under the ε-contamination model. Our approach improves over existing algorithms, which are not only suboptimal but also require stringent assumptions, including Lipschitz continuity and smoothness of individual sample functions. By contrast, our optimal algorithms do not require these stringent assumptions, assuming only population-level smoothness of the loss. Moreover, our algorithms can be adapted to handle the case in which the covariance parameter is unknown, and can be extended to nonsmooth population risks via convolutional smoothing. We complement our algorithmic developments with a tight information-theoretic lower bound for robust SCO.

Cite as

Changyu Gao, Andrew Lowy, Xingyu Zhou, and Stephen J. Wright. Optimal Rates for Robust Stochastic Convex Optimization. In 6th Symposium on Foundations of Responsible Computing (FORC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 329, pp. 9:1-9:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{gao_et_al:LIPIcs.FORC.2025.9,
  author =	{Gao, Changyu and Lowy, Andrew and Zhou, Xingyu and Wright, Stephen J.},
  title =	{{Optimal Rates for Robust Stochastic Convex Optimization}},
  booktitle =	{6th Symposium on Foundations of Responsible Computing (FORC 2025)},
  pages =	{9:1--9:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-367-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{329},
  editor =	{Bun, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.9},
  URN =		{urn:nbn:de:0030-drops-231369},
  doi =		{10.4230/LIPIcs.FORC.2025.9},
  annote =	{Keywords: Adversarial Robustness, Machine Learning, Optimization Algorithms, Robust Optimization, Stochastic Convex Optimization}
}

Document

Academic Track

DOI: 10.4230/OASIcs.SAIA.2024.12

A View on Vulnerabilites: The Security Challenges of XAI (Academic Track)

Authors: Elisabeth Pachl, Fabian Langer, Thora Markert, and Jeanette Miriam Lorenz

Published in: OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024)

Abstract

Modern deep learning methods have long been considered as black-boxes due to their opaque decision-making processes. Explainable Artificial Intelligence (XAI), however, has turned the tables: it provides insight into how these models work, promoting transparency that is crucial for accountability. Yet, recent developments in adversarial machine learning have highlighted vulnerabilities in XAI methods, raising concerns about security, reliability and trustworthiness, particularly in sensitive areas like healthcare and autonomous systems. Awareness of the potential risks associated with XAI is needed as its adoption increases, driven in part by the need to enhance compliance to regulations. This survey provides a holistic perspective on the security and safety landscape surrounding XAI, categorizing research on adversarial attacks against XAI and the misuse of explainability to enhance attacks on AI systems, such as evasion and privacy breaches. Our contribution includes identifying current insecurities in XAI and outlining future research directions in adversarial XAI. This work serves as an accessible foundation and outlook to recognize potential research gaps and define future directions. It identifies data modalities, such as time-series or graph data, and XAI methods that have not been extensively investigated for vulnerabilities in current research.

Cite as

Elisabeth Pachl, Fabian Langer, Thora Markert, and Jeanette Miriam Lorenz. A View on Vulnerabilites: The Security Challenges of XAI (Academic Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 12:1-12:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{pachl_et_al:OASIcs.SAIA.2024.12,
  author =	{Pachl, Elisabeth and Langer, Fabian and Markert, Thora and Lorenz, Jeanette Miriam},
  title =	{{A View on Vulnerabilites: The Security Challenges of XAI}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{12:1--12:23},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-357-7},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{126},
  editor =	{G\"{o}rge, Rebekka and Haedecke, Elena and Poretschkin, Maximilian and Schmitz, Anna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.12},
  URN =		{urn:nbn:de:0030-drops-227523},
  doi =		{10.4230/OASIcs.SAIA.2024.12},
  annote =	{Keywords: Explainability, XAI, Transparency, Adversarial Machine Learning, Security, Vulnerabilities}
}

Document

DOI: 10.4230/DagRep.13.10.90

Network Attack Detection and Defense - AI-Powered Threats and Responses (Dagstuhl Seminar 23431)

Authors: Sven Dietrich, Frank Kargl, Hartmut König, Pavel Laskov, and Artur Hermann

Published in: Dagstuhl Reports, Volume 13, Issue 10 (2024)

Abstract

This report documents the program and the findings of Dagstuhl Seminar 23431 "Network Attack Detection and Defense - AI-Powered Threats and Responses". With the emergence of artificial intelligence (AI), attack detection and defense are taking on a new level of quality. Artificial intelligence will promote further automation of attacks. There are already examples of this, such as the Deep Locker malware. It is expected that we will soon face a situation in which malware and attacks will become more and more automated, intelligent, and AI-powered. Consequently, today’s threat response systems will become more and more inadequate, especially when they rely on manual intervention of security experts and analysts. The main objective of the seminar was to assess the state of the art and potentials that AI advances create for both attackers and defenders. The seminar continued the series of Dagstuhl events "Network Attack Detection and Defense" held in 2008, 2012, 2014, and 2016. The objectives of the seminar were threefold, namely (1) to investigate various scenarios of AI-based malware and attacks, (2) to debate trust in AI and modeling of threats against AI, and (3) to propose methods and strategies for AI-powered network defenses. At the seminar, which brought together participants from academia and industry, we stated that recent advances in artificial intelligence have opened up new possibilities for each of these directions. In general, more and more researchers in networking and security look at AI-based methods which made this a timely event to assess and categorize the state of the art as well as work towards a roadmap for future research. The outcome of the discussions and the proposed research directions are presented in this report.

Cite as

Sven Dietrich, Frank Kargl, Hartmut König, Pavel Laskov, and Artur Hermann. Network Attack Detection and Defense - AI-Powered Threats and Responses (Dagstuhl Seminar 23431). In Dagstuhl Reports, Volume 13, Issue 10, pp. 90-129, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{dietrich_et_al:DagRep.13.10.90,
  author =	{Dietrich, Sven and Kargl, Frank and K\"{o}nig, Hartmut and Laskov, Pavel and Hermann, Artur},
  title =	{{Network Attack Detection and Defense - AI-Powered Threats and Responses (Dagstuhl Seminar 23431)}},
  pages =	{90--129},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{10},
  editor =	{Dietrich, Sven and Kargl, Frank and K\"{o}nig, Hartmut and Laskov, Pavel and Hermann, Artur},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.10.90},
  URN =		{urn:nbn:de:0030-drops-198365},
  doi =		{10.4230/DagRep.13.10.90},
  annote =	{Keywords: artificial intelligence, cybersecurity, intrusion detection, machine learning}
}

Document

DOI: 10.4230/DagRep.12.7.41

Security of Machine Learning (Dagstuhl Seminar 22281)

Authors: Battista Biggio, Nicholas Carlini, Pavel Laskov, Konrad Rieck, and Antonio Emanuele Cinà

Published in: Dagstuhl Reports, Volume 12, Issue 7 (2023)

Abstract

Machine learning techniques, especially deep neural networks inspired by mathematical models of human intelligence, have reached an unprecedented success on a variety of data analysis tasks. The reliance of critical modern technologies on machine learning, however, raises concerns on their security, especially since powerful attacks against mainstream learning algorithms have been demonstrated since the early 2010s. Despite a substantial body of related research, no comprehensive theory and design methodology is currently known for the security of machine learning. The proposed seminar aims at identifying potential research directions that could lead to building the scientific foundation for the security of machine learning. By bringing together researchers from machine learning and information security communities, the seminar is expected to generate new ideas for security assessment and design in the field of machine learning.

Cite as

Battista Biggio, Nicholas Carlini, Pavel Laskov, Konrad Rieck, and Antonio Emanuele Cinà. Security of Machine Learning (Dagstuhl Seminar 22281). In Dagstuhl Reports, Volume 12, Issue 7, pp. 41-61, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{biggio_et_al:DagRep.12.7.41,
  author =	{Biggio, Battista and Carlini, Nicholas and Laskov, Pavel and Rieck, Konrad and Cin\`{a}, Antonio Emanuele},
  title =	{{Security of Machine Learning (Dagstuhl Seminar 22281)}},
  pages =	{41--61},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{7},
  editor =	{Biggio, Battista and Carlini, Nicholas and Laskov, Pavel and Rieck, Konrad and Cin\`{a}, Antonio Emanuele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.7.41},
  URN =		{urn:nbn:de:0030-drops-176117},
  doi =		{10.4230/DagRep.12.7.41},
  annote =	{Keywords: adversarial machine learning, machine learning security}
}

Document

DOI: 10.4230/DagMan.3.1.1

Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)

Authors: Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, and Blaine Nelson

Published in: Dagstuhl Manifestos, Volume 3, Issue 1 (2013)

Abstract

The study of learning in adversarial environments is an emerging discipline at the juncture between machine learning and computer security. The interest in learning-based methods for security- and system-design applications comes from the high degree of complexity of phenomena underlying the security and reliability of computer systems. As it becomes increasingly difficult to reach the desired properties solely using statically designed mechanisms, learning methods are being used more and more to obtain a better understanding of various data collected from these complex systems. However, learning approaches can be evaded by adversaries, who change their behavior in response to the learning methods. To-date, there has been limited research into learning techniques that are resilient to attacks with provable robustness guarantees The Perspectives Workshop, "Machine Learning Methods for Computer Security" was convened to bring together interested researchers from both the computer security and machine learning communities to discuss techniques, challenges, and future research directions for secure learning and learning-based security applications. As a result of the twenty-two invited presentations, workgroup sessions and informal discussion, several priority areas of research were identified. The open problems identified in the field ranged from traditional applications of machine learning in security, such as attack detection and analysis of malicious software, to methodological issues related to secure learning, especially the development of new formal approaches with provable security guarantees. Finally a number of other potential applications were pinpointed outside of the traditional scope of computer security in which security issues may also arise in connection with data-driven methods. Examples of such applications are social media spam, plagiarism detection, authorship identification, copyright enforcement, computer vision (particularly in the context of biometrics), and sentiment analysis.

Cite as

Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, and Blaine Nelson. Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371). In Dagstuhl Manifestos, Volume 3, Issue 1, pp. 1-30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@Article{joseph_et_al:DagMan.3.1.1,
  author =	{Joseph, Anthony D. and Laskov, Pavel and Roli, Fabio and Tygar, J. Doug and Nelson, Blaine},
  title =	{{Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)}},
  pages =	{1--30},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2013},
  volume =	{3},
  number =	{1},
  editor =	{Joseph, Anthony D. and Laskov, Pavel and Roli, Fabio and Tygar, J. Doug and Nelson, Blaine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.3.1.1},
  URN =		{urn:nbn:de:0030-drops-43569},
  doi =		{10.4230/DagMan.3.1.1},
  annote =	{Keywords: Adversarial Learning, Computer Security, Robust Statistical Learning, Online Learning with Experts, Game Theory, Learning Theory}
}

Document

DOI: 10.4230/DagRep.2.9.109

Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)

Authors: Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, and Blaine Nelson

Published in: Dagstuhl Reports, Volume 2, Issue 9 (2013)

Abstract

The study of learning in adversarial environments is an emerging discipline at the juncture between machine learning and computer security that raises new questions within both fields. The interest in learning-based methods for security and system design applications comes from the high degree of complexity of phenomena underlying the security and reliability of computer systems. As it becomes increasingly difficult to reach the desired properties by design alone, learning methods are being used to obtain a better understanding of various data collected from these complex systems. However, learning approaches can be co-opted or evaded by adversaries, who change to counter them. To-date, there has been limited research into learning techniques that are resilient to attacks with provable robustness guarantees making the task of designing secure learning-based systems a lucrative open research area with many challenges. The Perspectives Workshop, ``Machine Learning Methods for Computer Security'' was convened to bring together interested researchers from both the computer security and machine learning communities to discuss techniques, challenges, and future research directions for secure learning and learning-based security applications. This workshop featured twenty-two invited talks from leading researchers within the secure learning community covering topics in adversarial learning, game-theoretic learning, collective classification, privacy-preserving learning, security evaluation metrics, digital forensics, authorship identification, adversarial advertisement detection, learning for offensive security, and data sanitization. The workshop also featured workgroup sessions organized into three topic: machine learning for computer security, secure learning, and future applications of secure learning.

Cite as

Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, and Blaine Nelson. Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371). In Dagstuhl Reports, Volume 2, Issue 9, pp. 109-130, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@Article{joseph_et_al:DagRep.2.9.109,
  author =	{Joseph, Anthony D. and Laskov, Pavel and Roli, Fabio and Tygar, J. Doug and Nelson, Blaine},
  title =	{{Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)}},
  pages =	{109--130},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2013},
  volume =	{2},
  number =	{9},
  editor =	{Joseph, Anthony D. and Laskov, Pavel and Roli, Fabio and Tygar, J. Doug and Nelson, Blaine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.2.9.109},
  URN =		{urn:nbn:de:0030-drops-37908},
  doi =		{10.4230/DagRep.2.9.109},
  annote =	{Keywords: Adversarial Learning, Computer Security, Robust Statistical Learning, Online Learning with Experts, Game Theory, Learning Theory}
}

Document

DOI: 10.4230/DagSemProc.08102.8

8. 08102 Manifesto – Perspectives Workshop: Network Attack Detection and Defense

Authors: Georg Carle, Falko Dressler, Richard A. Kemmerer, Hartmut Koenig, Christopher Kruegel, and Pavel Laskov

Published in: Dagstuhl Seminar Proceedings, Volume 8102, Perspectives Workshop: Network Attack Detection and Defense (2008)

Abstract

This manifesto is the result of the Perspective Workshop Network Attack Detection and Defense held in Schloss Dagstuhl (Germany) from March 2nd – 6th, 2008. The participants of the workshop represent researchers from Austria, France, Norway, the Switzerland, the United States, and Germany who work actively in the field of intrusion detection and network monitoring. The workshop attendee’s opinion was that intrusion detection and flow analysis, which have been developed as complementary approaches for the detection of network attacks, should more strongly combine event detection and correlation techniques to better meet future challenges in future reactive security. The workshop participants considered various perspectives to envision future network attack detection and defense. The following topics are seen as important in the future: the development of early warning systems, the introduction of situation awareness, the improvement of measurement technology, taxonomy of attacks, the application of intrusion and fraud detection for web services, and anomaly detection. In order to realize those visions the state of the art, the challenges, and research priorities were identified for each topic by working groups. The outcome of the discussion is summarized in working group papers which are published in the workshop proceedings. The papers were compiled by the editors to this manifesto.

Cite as

Georg Carle, Falko Dressler, Richard A. Kemmerer, Hartmut Koenig, Christopher Kruegel, and Pavel Laskov. 8. 08102 Manifesto – Perspectives Workshop: Network Attack Detection and Defense. In Perspectives Workshop: Network Attack Detection and Defense. Dagstuhl Seminar Proceedings, Volume 8102, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{carle_et_al:DagSemProc.08102.8,
  author =	{Carle, Georg and Dressler, Falko and Kemmerer, Richard A. and Koenig, Hartmut and Kruegel, Christopher and Laskov, Pavel},
  title =	{{8. 08102 Manifesto – Perspectives Workshop: Network Attack Detection and Defense}},
  booktitle =	{Perspectives Workshop: Network Attack Detection and Defense},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8102},
  editor =	{Georg Carle and Falko Dressler and Richard A. Kemmerer and Hartmut K\"{o}nig and Christopher Kruegel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08102.8},
  URN =		{urn:nbn:de:0030-drops-14917},
  doi =		{10.4230/DagSemProc.08102.8},
  annote =	{Keywords: Manifesto of the Dagstuhl Perspective Workshop, March 2nd - 6th, 2008}
}

8 Search Results for "Laskov, Pavel"

Security and Privacy of Large Language Models (Dagstuhl Seminar 25461)

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Optimal Rates for Robust Stochastic Convex Optimization

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A View on Vulnerabilites: The Security Challenges of XAI (Academic Track)

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Network Attack Detection and Defense - AI-Powered Threats and Responses (Dagstuhl Seminar 23431)

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Security of Machine Learning (Dagstuhl Seminar 22281)

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Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)

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Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)

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8. 08102 Manifesto – Perspectives Workshop: Network Attack Detection and Defense

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