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ELLIS PhD Student at CISPA Helmholtz Center for Information Security, working on agentic and self-improving systems for cyber security.

Academic Activities

Academic activities of Christoph R. Landolt: reviewing, talks, media appearances, teaching, and student supervision.

Publications

Peer-reviewed publications by Christoph R. Landolt on multi-agent reinforcement learning, adversarial AI, and security for foundation models.

Teaching

Teaching by Christoph R. Landolt, including Machine Learning in Cybersecurity at CISPA.

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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portfolio

publications

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications

Published in ICMCIS 2025 - International Conference on Military Communication and Information Systems, 2025

Multi-Agent Reinforcement Learning (MARL) has shown great potential as an adaptive solution for addressing modern cybersecurity challenges. MARL enables decentralized, adaptive, and collaborative defense strategies and provides an automated mechanism to combat dynamic, coordinated, and sophisticated threats. This survey investigates the current state of research in MARL applications for automated cyber defense (ACD), focusing on intruder detection and lateral movement containment. Additionally, it examines the role of Autonomous Intelligent Cyber-defense Agents (AICA) and Cyber Gyms in training and validating MARL agents. Finally, the paper outlines existing challenges, such as scalability and adversarial robustness, and proposes future research directions. This also discusses how MARL integrates in AICA to provide adaptive, scalable, and dynamic solutions to counter the increasingly sophisticated landscape of cyber threats. It highlights the transformative potential of MARL in areas like intrusion detection and lateral movement containment, and underscores the value of Cyber Gyms for training and validation of AICA.

Recommended citation: Christoph R Landolt, Christoph Würsch, Roland Meier, Alain Mermoud, Julian Jang-Jaccard. (2025). "Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications." ICMCIS 2025. 1(1). https://arxiv.org/pdf/2505.19837

Securing RAG: A Risk Assessment and Mitigation Framework

Published in IEEE Swiss Conference on Data Science 2025, 2025

Retrieval Augmented Generation (RAG) has emerged as the de facto industry standard for user-facing NLP applications, offering the ability to integrate data without re-training or fine-tuning Large Language Models (LLMs). This capability enhances the quality and accuracy of responses but also introduces novel security and privacy challenges, particularly when sensitive data is integrated. With the rapid adoption of RAG, securing data and services has become a critical priority. This paper first reviews the vulnerabilities of RAG pipelines, and outlines the attack surface from data pre-processing and data storage management to integration with LLMs. The identified risks are then paired with corresponding mitigations in a structured overview. In a second step, the paper develops a framework that combines RAG-specific security considerations, with existing general security guidelines, industry standards, and best practices. The proposed framework aims to guide the implementation of robust, compliant, secure, and trustworthy RAG systems.

Recommended citation: Lukas Ammann, Sara Ott, Christoph R Landolt, Marco P Lehmann. (2025). "Securing RAG: A Risk Assessment and Mitigation Framework." IEEE Swiss Conference on Data Science 2025. 1(1). https://arxiv.org/pdf/2505.08728

Enhancing Cyber Attack Autonomy Through Multi-Agent Reinforcement Learning

Published in CyCon 2026 — 18th International Conference on Cyber Conflict, NATO CCDCOE, 2026

Advances in artificial intelligence and machine learning are transforming offensive cybersecurity, enabling the creation of autonomous intelligent cyber agents (AICAs) for offensive operations that overcome the limitations of traditional, static red-teaming playbooks. This work leverages deep multi-agent reinforcement learning (MARL) to implement autonomous cyber agents capable of coordinating sophisticated distributed attacks in dynamic environments. Using the network attack simulation (NASim) environment as a testbed, this paper highlights the limitations of single-agent approaches and demonstrates MARL’s potential to enable decentralized, collaborative strategies. Multi-agent systems were implemented for collaborative red team exercises, such as lateral movement and capture the flag. The results indicate that MARL-based agents successfully learn coordinated attack patterns in small-scale environments, which improves stealth and adaptation. However, while MARL demonstrates potential for decentralized collaboration, our evaluation reveals significant scalability challenges as network complexity increases. These findings underscore the importance of AICA in advancing offensive capabilities while identifying the stabilization of multi-agent training in large-scale topologies as a primary bottleneck for future research.

Recommended citation: Christoph R Landolt, Julian Jang-Jaccard, Valentin Mulder, Roland Meier, Christoph Würsch, Mario Fritz. (2026). "Enhancing Cyber Attack Autonomy Through Multi-Agent Reinforcement Learning." CyCon 2026: Securing Tomorrow, 18th International Conference on Cyber Conflict, 357–380. /files/2026_CyCon_MARL.pdf

The Oracle’s Gambit: A Game-Theoretic Framework for Responsible AI Release

Published in arXiv preprint, 2026

Responsible vulnerability disclosure can secure the defender’s head start by controlling when a vulnerability becomes public. However, this status quo is now challenged by increases in capability of AI models, which benefits both defenders and adversaries. When both sides draw their capability from the same AI model, the defender’s head start depends on the lab’s decision to release the model, and the question becomes not whether to release but how. Existing safety frameworks govern only the deploy-or-withhold threshold and leave the timing of release unmodeled. We cast this decision as a bilevel Stackelberg game in which a lab commits to a window that sets each side’s capability over time in a downstream contest between defender and adversary. Defender welfare turns on the capability gap, not the shared level. Handing one model to both sides can trap the defender in a Red Queen’s race, whereas a pre-release to the defender alone creates a protective gap, and the lab’s optimal window balances this welfare gain against the opportunity cost of delaying release. For dual-use models, the lever is the sequencing of access, not the deployment threshold.

Recommended citation: Christoph R Landolt, Tobias Lorenz, Marta Kwiatkowska, Mario Fritz. (2026). "The Oracle's Gambit: A Game-Theoretic Framework for Responsible AI Release." arXiv preprint arXiv:2607.05442. https://arxiv.org/pdf/2607.05442

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.