Author

Cristina Nita-Rotaru

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#artificial intelligence Preprint Sep 2026

PANDA: A Decentralized Architecture with Flexible Orchestration for Scalable, Fault-Tolerant Multi-Agent Systems

Existing architectures for LLM-based multi-agent systems (MAS) cannot reliably and efficiently solve multi-step tasks at scale: they struggle to support large numbers of agents and concurrent tasks, tolerate failures, govern agent interactions, and accommodate the diverse planning and execution patterns different tasks...

Matthew Laws, Cristina Nita-Rotaru · 0 citations

#artificial intelligence Preprint Sep 2026

Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents

Several open research questions are identified, including how to efficiently record incidents and how to determine whether vulnerabilities and incidents generalize, and privacy requirements are summarized and research directions for the secure and trustworthy deployment of AI agents are outlined.

Anastasia Pustozerova, E. Bagdasarian, Luca Beurer-Kellner et al. · 0 citations

Review Jul 2026

ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors via Adversarial Code Comments

AlIBI is presented, an automated adaptive black-box attack framework that generates and iteratively refines adversarial comments using detector reasoning and feedback and is motivated to motivate security-aware designs that carefully calibrate trust between natural-language context and program evidence.

Zixuan Wu, Cristina Nita-Rotaru · arXiv.org · 0 citations