arXiv:2606.27694v1 Announce Type: cross Abstract: Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited b…
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arXiv:2606.27694v1 Announce Type: cross Abstract: Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited b…
arXiv:2606.27558v1 Announce Type: cross Abstract: Fairness measurements in the form of disaggregated evaluations often rely on demographic signals that are legally constrained or culturally sensitive.…
arXiv:2606.28153v1 Announce Type: new Abstract: Jailbreak attacks bypass LLM safety alignment, yet their mechanisms remain poorly understood. We provide evidence that attacks do not comprehensively el…
arXiv:2606.28079v1 Announce Type: new Abstract: The rapid proliferation of automated, multi-vector malware threats poses a significant risk to heterogeneous, resource constrained cyber-physical networ…
arXiv:2606.28061v1 Announce Type: new Abstract: Large language models (LLMs) have increasingly moved from standalone text generation systems to agents that invoke external tools, access environments, …
arXiv:2606.28006v1 Announce Type: new Abstract: Cyber deception research has focused on improving honeypot deception capabilities to increase attacker engagement and extend their interactions to colle…
arXiv:2606.27990v1 Announce Type: new Abstract: LLM-based SSH honeypots can generate believable interactions, but evaluations indicate they remain somewhat identifiable to determined attackers, indica…
arXiv:2606.27976v1 Announce Type: new Abstract: Dense embeddings underpin semantic search and RAG, yet a leaked vector store hands much of the underlying text back to whoever holds it. The attacks tha…
arXiv:2606.27966v1 Announce Type: new Abstract: Cyber deception research often assumes that a decoy can be placed wherever there is attacker behavior. This work tests that assumption across MITRE ATT&…
arXiv:2606.27936v1 Announce Type: new Abstract: The widespread collection of fine-grained location data by commercial data brokers creates a re-identification risk that is not widely recognised by the…
arXiv:2606.27934v1 Announce Type: new Abstract: Performance numbers reported for hardware are accepted on trust: the reader cannot recompute them, the apparatus is gone, and the silicon itself can be …
arXiv:2606.27819v1 Announce Type: new Abstract: Time-triggered communication protocols rely on trusted components known as guardians to enforce adherence to predetermined network schedules. Network-ag…
arXiv:2606.27803v1 Announce Type: new Abstract: Fuzzy Labeled Private Set Intersection (FLPSI) lets a receiver learn the labels of enrolled records similar to its query, and nothing else. Construction…
arXiv:2606.27704v1 Announce Type: new Abstract: TinyML models deployed on edge devices are increasingly adopted in safety/security-critical applications, making them a prime target for adversarial exa…
arXiv:2606.27567v1 Announce Type: new Abstract: Prompt injection is the top security risk for LLM-integrated applications, yet every defense proposed so far has been broken. We prove this is not a coi…
arXiv:2606.27511v1 Announce Type: new Abstract: Large Language Model (LLM)-based question-answering (QA) systems are increasingly deployed in sensitive domains such as healthcare, mental health counse…
How to Secure Enterprise AI Infrastructure in 2026 Kings Research
arXiv:2606.26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually da…
arXiv:2606.26549v1 Announce Type: new Abstract: Long-term time series forecasting (LTSF) plays a crucial role in fields such as energy management, finance, and traffic prediction. Transformer-based mo…
arXiv:2606.26523v1 Announce Type: new Abstract: We develop a framework for interpreting AI systems as agents, drawing on the philosophical tradition of radical interpretation and the tools of mechanis…
arXiv:2606.26519v1 Announce Type: new Abstract: Large language models (LLMs) can make scientific software easier to use. However, a general model does not automatically know which measurements a parti…
arXiv:2606.26518v1 Announce Type: new Abstract: This tutorial paper provides a step-by-step, reproducible walkthrough of NeuraDock Agent, an open-source EEG agent focused on Alpha dynamics and visual …
arXiv:2606.26502v1 Announce Type: new Abstract: Large reasoning models (LRMs) take longer on harder problems, just as humans do. This surface similarity hides an opposite pattern within items. When an…
arXiv:2606.26494v1 Announce Type: new Abstract: Medical AI remains organized around isolated models, whereas clinical care requires accountable capabilities that persist across time. We propose clinic…