Tootfinder

Note to web designers: if you set a size for body text fonts, a substantial part of your audience won't be able to read your page at all, so you've lost them; and more tech-savvy users will either zoom the page, breaking your clever design, or remove your styling altogether (which is what I tend to do).
The user knows what size of font the user can read. The user has their preferences set up so they can read their screen. Trust the user.
And if that breaks your design, yo…

Adaptive Migration Decision for Multi-Tenant Memory Systems
Hyungjun Cho, Igjae Kim, Kwanghoon Choi, Hongjin Kim, Wonjae Lee, Junhyeok Im, Jinin So, Jaehyuk Huh
https://arxiv.org/abs/2505.09164

Adaptive Migration Decision for Multi-Tenant Memory Systems
Tiered memory systems consisting of fast small memory and slow large memory have emerged to provide high capacity memory in a cost-effective way. The effectiveness of tiered memory systems relies on how many memory accesses can be absorbed by the fast first-tier memory by page migration. The recent studies proposed several different ways of detecting hot pages and migrating them efficiently. However, our investigation shows that page migration is not always beneficial as it has the associated c…

[2025-05-15 Thu (UTC), no new articles found for q-fin.TR Trading and Market Microstructure]
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[2025-05-15 Thu (UTC), no new articles found for cs.MS Mathematical Software]
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[2025-05-15 Thu (UTC), no new articles found for math.HO History and Overview]
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My Saturday morning: remove 18 words - 16 of them adjectives - from the last 1 page I wrote. Done. What was I thinking!

[2025-05-14 Wed (UTC), 1 new article found for q-fin.PR Pricing of Securities]
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これ読むといいんじゃないかな。うえのさんの。
--
二つのクリスマス. ——何かを「待ちのぞむ」ということ | by ueno | Medium
https://oryzivora.page/二つのクリスマス-750d0b08d274?gi=705df5f6d027

[2025-05-15 Thu (UTC), no new articles found for math.GN General Topology]
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[2025-05-14 Wed (UTC), 1 new article found for cs.OH Other Computer Science]
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[2025-05-15 Thu (UTC), no new articles found for cs.GL General Literature]
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[2025-05-14 Wed (UTC), 1 new article found for q-fin.RM Risk Management]
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[2025-05-15 Thu (UTC), no new articles found for q-fin.GN General Finance]
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[2025-05-15 Thu (UTC), 1 new article found for q-bio.MN Molecular Networks]
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[2025-05-15 Thu (UTC), no new articles found for q-fin.PM Portfolio Management]
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Minimality and computability of languages of G-shifts
Djamel Eddine Amir, Benjamin Hellouin de Menibus
https://arxiv.org/abs/2506.10610 https://

Minimality and computability of languages of G-shifts
Motivated by the notion of strong computable type for sets in computable analysis, we define the notion of strong computable type for $G$-shifts, where $G$ is a finitely generated group with decidable word problem. A $G$-shift has strong computable type if one can compute its language from the complement of its language. We obtain a characterization of $G$-shifts with strong computable type in terms of a notion of minimality with respect to properties with a bounded computational complexity. We…

Encoding call-by-push-value in the pi-calculus
Benjamin Bennetzen, Nikolaj Rossander Kristensen, Peter Buus Steffensen
https://arxiv.org/abs/2506.10584 htt…

Encoding call-by-push-value in the pi-calculus
In this report we define an encoding of Levys call-by-push-value lambda-calculus (CBPV) in the pi-calculus, and prove that our encoding is both sound and complete. We present informal (by-hand) proofs of soundness, completeness, and all required lemmas. The encoding is specialized to the internal pi-calculus (pi-i-calculus) to circumvent certain challenges associated with using de Bruijn index in a formalization, and it also helps with bisimulation as early-, late- and open-bisimulation coincid…

Choreographic Quick Changes: First-Class Location (Set) Polymorphism
Ashley Samuelson, Andrew K. Hirsch, Ethan Cecchetti
https://arxiv.org/abs/2506.10913 h…

Choreographic Quick Changes: First-Class Location (Set) Polymorphism
Choreographic programming is a promising new paradigm for programming concurrent systems where a developer writes a single centralized program that compiles to individual programs for each node. Existing choreographic languages, however, lack critical features integral to modern systems, like the ability of one node to dynamically compute who should perform a computation and send that decision to others. This work addresses this gap with $λ_{QC}$, the first typed choreographic language with \e…

Faster CONGEST Approximation Algorithms for Maximum Weighted Independent Set in Sparse Graphs
Salwa Faour, Fabian Kuhn
https://arxiv.org/abs/2506.10845 htt…

Faster CONGEST Approximation Algorithms for Maximum Weighted Independent Set in Sparse Graphs
The maximum independent set problem is a classic optimization problem that has also been studied quite intensively in the distributed setting. While the problem is hard to approximate in general, there are good approximation algorithms known for several sparse graph families. In this paper, we consider deterministic distributed CONGEST algorithms for the weighted version of the problem in trees and graphs of bounded arboricity. For trees, we prove that the task of deterministically computing …

MARS: Processing-In-Memory Acceleration of Raw Signal Genome Analysis Inside the Storage Subsystem
Melina Soysal, Konstantina Koliogeorgi, Can Firtina, Nika Mansouri Ghiasi, Rakesh Nadig, Haiyu Mayo, Geraldo F. Oliveira, Yu Liang, Klea Zambaku, Mohammad Sadrosadati, Onur Mutlu
https://arxiv.org/abs/2506.10931

MARS: Processing-In-Memory Acceleration of Raw Signal Genome Analysis Inside the Storage Subsystem
Raw signal genome analysis (RSGA) has emerged as a promising approach to enable real-time genome analysis by directly analyzing raw electrical signals. However, rapid advancements in sequencing technologies make it increasingly difficult for software-based RSGA to match the throughput of raw signal generation. This paper demonstrates that while hardware acceleration techniques can significantly accelerate RSGA, the high volume of genomic data shifts the performance and energy bottleneck from co…

A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures
Maximilian M\"uhlbauer, Freek Stulp, Sylvain Calinon, Alin Albu-Sch\"affer, Jo\~ao Silv\'erio
https://arxiv.org/abs/2506.10239

A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures
Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned or perceived uncertainty. While keeping the human in the loop remains essential, for instance, to ensure high precision, partial automation of certain task phases is critical for productivity. We present a unified framework for probabilistic VFs that seamlessly switches between manual fixtures, semi-automated fixtures (with the human handling precise…

TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks
Xiaoxing Mo, Yuxuan Cheng, Nan Sun, Leo Yu Zhang, Wei Luo, Shang Gao
https://arxiv.org/abs/2506.10722

TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks
Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, where attackers implant hidden triggers during training to maliciously control model behavior. Topological Evolution Dynamics (TED) has recently emerged as a powerful tool for detecting backdoor attacks in DNNs. However, TED can be vulnerable to backdoor attacks that adaptively distort topological representation distributions across network layers. To address this limitation, we propose TED-LaST (Topological Evolution Dynamics agai…

[2025-05-15 Thu (UTC), 2 new articles found for q-bio.GN Genomics]
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TrioXpert: An automated incident management framework for microservice system
Yongqian Sun, Yu Luo, Xidao Wen, Yuan Yuan, Xiaohui Nie, Shenglin Zhang, Tong Liu, Xi Luo
https://arxiv.org/abs/2506.10043

TrioXpert: An automated incident management framework for microservice system
Automated incident management plays a pivotal role in large-scale microservice systems. However, many existing methods rely solely on single-modal data (e.g., metrics, logs, and traces) and struggle to simultaneously address multiple downstream tasks, including anomaly detection (AD), failure triage (FT), and root cause localization (RCL). Moreover, the lack of clear reasoning evidence in current techniques often leads to insufficient interpretability. To address these limitations, we propose T…

[2025-05-14 Wed (UTC), no new articles found for q-fin.TR Trading and Market Microstructure]
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[2025-05-15 Thu (UTC), 1 new article found for cs.OS Operating Systems]
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[2025-05-14 Wed (UTC), no new articles found for cs.MS Mathematical Software]
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[2025-05-14 Wed (UTC), no new articles found for math.HO History and Overview]
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[2025-05-14 Wed (UTC), no new articles found for stat.OT Other Statistics]
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[2025-05-14 Wed (UTC), no new articles found for cs.OS Operating Systems]
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[2025-05-14 Wed (UTC), no new articles found for q-fin.PM Portfolio Management]
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Chance and Mass Interpretations of Probabilities in Markov Decision Processes (Extended Version)
Yun Chen Tsai, Kittiphon Phalakarn, S. Akshay, Ichiro Hasuo
https://arxiv.org/abs/2506.10377

Chance and Mass Interpretations of Probabilities in Markov Decision Processes (Extended Version)
Markov decision processes (MDPs) are a popular model for decision-making in the presence of uncertainty. The conventional view of MDPs in verification treats them as state transformers with probabilities defined over sequences of states and with schedulers making random choices. An alternative view, especially well-suited for modeling dynamical systems, defines MDPs as distribution transformers with schedulers distributing probability masses. Our main contribution is a unified semantical framew…

StepProof: Step-by-step verification of natural language mathematical proofs
Xiaolin Hu, Qinghua Zhou, Bogdan Grechuk, Ivan Y. Tyukin
https://arxiv.org/abs/2506.10558

StepProof: Step-by-step verification of natural language mathematical proofs
Interactive theorem provers (ITPs) are powerful tools for the formal verification of mathematical proofs down to the axiom level. However, their lack of a natural language interface remains a significant limitation. Recent advancements in large language models (LLMs) have enhanced the understanding of natural language inputs, paving the way for autoformalization - the process of translating natural language proofs into formal proofs that can be verified. Despite these advancements, existing aut…

Hazel Deriver: A Live Editor for Constructing Rule-Based Derivations
Zhiyao Zhong, Cyrus Omar
https://arxiv.org/abs/2506.10781 https://

Hazel Deriver: A Live Editor for Constructing Rule-Based Derivations
Students in programming languages and formal logic courses often struggle with constructing rule-based derivation trees due to the complexity of applying inference rules, the lack of immediate feedback, and the manual effort required for handwritten proofs. We present Hazel Deriver, a live, web-based editor designed to scaffold derivation construction through multiple layers of support. Built on the Hazel live programming environment, it provides a structured, interactive experience that encour…

Structural Parameterizations of $k$-Planarity
Tatsuya Gima, Yasuaki Kobayashi, Yuto Okada
https://arxiv.org/abs/2506.10717 https://ar…

Structural Parameterizations of $k$-Planarity
The concept of $k$-planarity is extensively studied in the context of Beyond Planarity. A graph is $k$-planar if it admits a drawing in the plane in which each edge is crossed at most $k$ times. The local crossing number of a graph is the minimum integer $k$ such that it is $k$-planar. The problem of determining whether an input graph is $1$-planar is known to be NP-complete even for near-planar graphs [Cabello and Mohar, SIAM J. Comput. 2013], that is, the graphs obtained from planar graphs by…

Towards Zero-Stall Matrix Multiplication on Energy-Efficient RISC-V Clusters for Machine Learning Acceleration
Luca Colagrande, Lorenzo Leone, Maximilian Coco, Andrei Deaconeasa, Luca Benini
https://arxiv.org/abs/2506.10921

Towards Zero-Stall Matrix Multiplication on Energy-Efficient RISC-V Clusters for Machine Learning Acceleration
The growing computational demands of machine learning (ML) workloads have driven the design of ML accelerators aiming at an optimal tradeoff between efficiency and flexibility. A widely explored architecture for flexible ML accelerators is based on clusters of lightweight instruction processors sharing multi-banked L1 memory, augmented with specialized instruction extensions for key ML-related computations, such as matrix multiplication (matmul). However, instruction extensions should be couple…

A Navigation Framework Utilizing Vision-Language Models
Yicheng Duan, Kaiyu tang
https://arxiv.org/abs/2506.10172 https://arxiv.org/p…

A Navigation Framework Utilizing Vision-Language Models
Vision-and-Language Navigation (VLN) presents a complex challenge in embodied AI, requiring agents to interpret natural language instructions and navigate through visually rich, unfamiliar environments. Recent advances in large vision-language models (LVLMs), such as CLIP and Flamingo, have significantly improved multimodal understanding but introduced new challenges related to computational cost and real-time deployment. In this project, we propose a modular, plug-and-play navigation framework…

Commitment Schemes for Multi-Party Computation
Ioan Ionescu, Ruxandra F. Olimid
https://arxiv.org/abs/2506.10721 https://arxiv.org/pd…

Commitment Schemes for Multi-Party Computation
The paper presents an analysis of Commitment Schemes (CSs) used in Multi-Party Computation (MPC) protocols. While the individual properties of CSs and the guarantees offered by MPC have been widely studied in isolation, their interrelation in concrete protocols and applications remains mostly underexplored. This paper presents the relation between the two, with an emphasis on (security) properties and their impact on the upper layer MPC. In particular, we investigate how different types of CSs …

Rough Bergomi turns grey
Antoine Jacquier, Adriano Oliveri Orioles, Zan Zuric
https://arxiv.org/abs/2505.08623 https://arxiv.org/pdf/…

Rough Bergomi turns grey
We propose a tractable extension of the rough Bergomi model, replacing the fractional Brownian motion with a generalised grey Brownian motion, which we show to be reminiscent of models with stochastic volatility of volatility. This extension breaks away from the log-Normal assumption of rough Bergomi, thereby making it a viable suggestion for the Equity Holy Grail -- the joint SPX/VIX options calibration. For this new (class of) model(s), we provide semi-closed and asymptotic formulae for SPX a…

[2025-05-15 Thu (UTC), no new articles found for nlin.CG Cellular Automata and Lattice Gases]
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Opportunities and Applications of GenAI in Smart Cities: A User-Centric Survey
Ankit Shetgaonkar, Dipen Pradhan, Lakshit Arora, Sanjay Surendranath Girija, Shashank Kapoor, Aman Raj
https://arxiv.org/abs/2505.08034

Opportunities and Applications of GenAI in Smart Cities: A User-Centric Survey
The proliferation of IoT in cities, combined with Digital Twins, creates a rich data foundation for Smart Cities aimed at improving urban life and operations. Generative AI (GenAI) significantly enhances this potential, moving beyond traditional AI analytics and predictions by processing multimodal content and generating novel outputs like text and simulations. Using specialized or foundational models, GenAI's natural language abilities such as Natural Language Understanding (NLU) and Natural L…

[2025-06-13 Fri (UTC), 2 new articles found for cs.FL Formal Languages and Automata Theory]
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Notes on applicative matching logic
Laurentiu Leustean
https://arxiv.org/abs/2506.10088 https://arxiv.org/pdf/2506.10088

Notes on applicative matching logic
Matching logic (ML) was developed by Grigore Roşu and collaborators as a logic for defining the formal semantics of programming languages and for specifying and reasoning about the behavior of programs. These lecture notes present basic definitions and results on applicative matching logic (AML), a functional variant of ML introduced recently by Xiaohong Chen and Grigore Roşu. They can be used as an introductory text in the theory of AML. Monk's textbook on mathematical logic has an enormous …

A Language-Agnostic Logical Relation for Message-Passing Protocols
Tesla Zhang, Sonya Simkin, Rui Li, Yue Yao, Stephanie Balzer
https://arxiv.org/abs/2506.10026

A Language-Agnostic Logical Relation for Message-Passing Protocols
Today's computing landscape has been gradually shifting to applications targeting distributed and *heterogeneous* systems, such as cloud computing and Internet of Things (IoT) applications. These applications are predominantly *concurrent*, employ *message-passing*, and interface with *foreign objects*, ranging from externally implemented code to actual physical devices such as sensors. Verifying that the resulting systems adhere to the intended protocol of interaction is challenging -- the usu…

New Approximation Guarantees for The Inventory Staggering Problem
Noga Alon, Danny Segev
https://arxiv.org/abs/2506.10339 https://arx…

New Approximation Guarantees for The Inventory Staggering Problem
Since its inception in the mid-60s, the inventory staggering problem has been explored and exploited in a wide range of application domains, such as production planning, stock control systems, warehousing, and aerospace/defense logistics. However, even with a rich history of academic focus, we are still very much in the dark when it comes to cornerstone computational questions around inventory staggering and to related structural characterizations, with our methodological toolbox being severely…

[2025-04-15 Tue (UTC), no new articles found for cs.GL General Literature]

EasyDRAM: An FPGA-based Infrastructure for Fast and Accurate End-to-End Evaluation of Emerging DRAM Techniques
O\u{g}uzhan Canpolat, Ataberk Olgun, David Novo, O\u{g}uz Ergin, Onur Mutlu
https://arxiv.org/abs/2506.10441

EasyDRAM: An FPGA-based Infrastructure for Fast and Accurate End-to-End Evaluation of Emerging DRAM Techniques
DRAM is a critical component of modern computing systems. Recent works propose numerous techniques (that we call DRAM techniques) to enhance DRAM-based computing systems' throughput, reliability, and computing capabilities (e.g., in-DRAM bulk data copy). Evaluating the system-wide benefits of DRAM techniques is challenging as they often require modifications across multiple layers of the computing stack. Prior works propose FPGA-based platforms for rapid end-to-end evaluation of DRAM techniques…

One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture
Marcos Abel Zuzu\'arregui, Mustafa Melih Toslak, Stefano Carpin
https://arxiv.org/abs/2506.10106

One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture
Artificial intelligence is transforming precision agriculture, offering farmers new tools to streamline their daily operations. While these technological advances promise increased efficiency, they often introduce additional complexity and steep learning curves that are particularly challenging for non-technical users who must balance tech adoption with existing workloads. In this paper, we present a natural language (NL) robotic mission planner that enables non-specialists to control heterogen…

This https://arxiv.org/abs/2502.00740 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Floating exercise boundaries for American options in time-inhomogeneous models
This paper examines a semi-analytical approach for pricing American options in time-inhomogeneous models characterized by negative interest rates (for equity/FX) or negative convenience yields (for commodities/cryptocurrencies). Under such conditions, exercise boundaries may exhibit a "floating" structure - dynamically appearing and disappearing. For example, a second exercise boundary could emerge within the computational domain and subsequently both could collapse, demanding specialized prici…

GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems
Davide Maffiola, Stefano Longari, Michele Carminati, Mara Tanelli, Stefano Zanero
https://arxiv.org/abs/2506.10665

GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems
Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time data, such as in traffic information management. Traditionally, road traffic information has been collected using on-site sensors. However, crowd-sourcing traffic information from onboard sensors or smartphones has become a viable alternative. State-of-the-art solutions currently follow a centralized model where only the…

[2025-05-14 Wed (UTC), no new articles found for nlin.CG Cellular Automata and Lattice Gases]
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Growing a Modular Framework for Modal Systems- HOLMS: a HOL Light Library
Antonella Bilotta
https://arxiv.org/abs/2506.10048 https://…

Growing a Modular Framework for Modal Systems- HOLMS: a HOL Light Library
The present dissertation introduces the research project on HOLMS (\textbf{HOL} Light Library for \textbf{M}odal \textbf{S}ystems), a growing modular framework for modal reasoning within the HOL Light proof assistant. To provide an accessible introduction to the library, the fundamentals of modal logic are outlined first, followed by a concise manual for the proof assistant itself. The core contribution of this work on HOLMS is the development of a unified and modular strategy for proving adequ…

This https://arxiv.org/abs/2410.14839 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Multi-Task Dynamic Pricing in Credit Market with Contextual Information
We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corporate bonds, government bonds, loans, and other credit-related securities. A major challenge in pricing these securities stems from their infrequent trading and the lack of transparency in over-the-counter (OTC) markets, which leads to insufficient data for individual pricing. Nevertheless, many securities share structural similarities that can be exploited.…

From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop
Jordi de la Torre
https://arxiv.org/abs/2506.10021 https://

From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop
We propose a novel architecture for integrating large language models (LLMs) with a persistent, interactive Lisp environment. This setup enables LLMs to define, invoke, and evolve their own tools through programmatic interaction with a live REPL. By embedding Lisp expressions within generation and intercepting them via a middleware layer, the system allows for stateful external memory, reflective programming, and dynamic tool creation. We present a design framework and architectural principles …

[2025-06-13 Fri (UTC), 3 new articles found for cs.DS Data Structures and Algorithms]
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[2025-04-14 Mon (UTC), no new articles found for cs.GL General Literature]

CarbonSet: A Dataset to Analyze Trends and Benchmark the Sustainability of CPUs and GPUs
Jiajun Hu, Chetan Choppali Sudarshan, Vidya A. Chhabria, Aman Arora
https://arxiv.org/abs/2506.10373

CarbonSet: A Dataset to Analyze Trends and Benchmark the Sustainability of CPUs and GPUs
Over the years, the chip industry has consistently developed high-performance processors to address the increasing demands across diverse applications. However, the rapid expansion of chip production has significantly increased carbon emissions, raising critical concerns about environmental sustainability. While researchers have previously modeled the carbon footprint (CFP) at both system and processor levels, a holistic analysis of sustainability trends encompassing the entire chip lifecycle r…

Estimating the Joint Probability of Scenario Parameters with Gaussian Mixture Copula Models
Christian Reichenb\"acher, Philipp Rank, Jochen Hipp, Oliver Bringmann
https://arxiv.org/abs/2506.10098

Estimating the Joint Probability of Scenario Parameters with Gaussian Mixture Copula Models
This paper presents the first application of Gaussian Mixture Copula Models to the statistical modeling of driving scenarios for the safety validation of automated driving systems. Knowledge of the joint probability distribution of scenario parameters is essential for scenario-based safety assessment, where risk quantification depends on the likelihood of concrete parameter combinations. Gaussian Mixture Copula Models bring together the multimodal expressivity of Gaussian Mixture Models and the…

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Aakanksha Saha, Martina Lindorfer, Juan Caballero
https://arxiv.org/abs/2506.10645

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Indicators of Compromise (IOCs) such as IP addresses, file hashes, and domain names are commonly used for threat detection and attribution. However, IOCs tend to be short-lived as they are easy to change. As a result, the cybersecurity community is shifting focus towards more persistent behavioral profiles, such as the Tactics, Techniques, and Procedures (TTPs) and the software used by a threat group. However, the distinctiveness and completeness of such behavioral profiles remain largely unexp…

[2025-06-13 Fri (UTC), 4 new articles found for cs.LO Logic in Computer Science]
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[2025-06-13 Fri (UTC), 4 new articles found for cs.PL Programming Languages]
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[2025-06-13 Fri (UTC), 4 new articles found for cs.AR Hardware Architecture]
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Leveraging LLMs for Mission Planning in Precision Agriculture
Marcos Abel Zuzu\'arregui, Stefano Carpin
https://arxiv.org/abs/2506.10093 https://

Leveraging LLMs for Mission Planning in Precision Agriculture
Robotics and artificial intelligence hold significant potential for advancing precision agriculture. While robotic systems have been successfully deployed for various tasks, adapting them to perform diverse missions remains challenging, particularly because end users often lack technical expertise. In this paper, we present an end-to-end system that leverages large language models (LLMs), specifically ChatGPT, to enable users to assign complex data collection tasks to autonomous robots using na…

CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment
Stefano Longari, Alessandro Pozone, Jessica Leoni, Mario Polino, Michele Carminati, Mara Tanelli, Stefano Zanero
https://arxiv.org/abs/2506.10638

CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment
In the last decades, Cyber-physical Systems (CPSs) have experienced a significant technological evolution and increased connectivity, at the cost of greater exposure to cyber-attacks. Since many CPS are used in safety-critical systems, such attacks entail high risks and potential safety harms. Although several defense strategies have been proposed, they rarely exploit the cyber-physical nature of the system. In this work, we exploit the nature of CPS by proposing CyFence, a novel architecture t…

[2025-06-13 Fri (UTC), 25 new articles found for cs.RO Robotics]
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Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation
Stefano Longari, Paolo Cerracchio, Michele Carminati, Stefano Zanero
https://arxiv.org/abs/2506.10620

Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation
The security of modern vehicles has become increasingly important, with the controller area network (CAN) bus serving as a critical communication backbone for various Electronic Control Units (ECUs). The absence of robust security measures in CAN, coupled with the increasing connectivity of vehicles, makes them susceptible to cyberattacks. While intrusion detection systems (IDSs) have been developed to counter such threats, they are not foolproof. Adversarial attacks, particularly evasion attac…

SoK: Evaluating Jailbreak Guardrails for Large Language Models
Xunguang Wang, Zhenlan Ji, Wenxuan Wang, Zongjie Li, Daoyuan Wu, Shuai Wang
https://arxiv.org/abs/2506.10597

SoK: Evaluating Jailbreak Guardrails for Large Language Models
Large Language Models (LLMs) have achieved remarkable progress, but their deployment has exposed critical vulnerabilities, particularly to jailbreak attacks that circumvent safety mechanisms. Guardrails--external defense mechanisms that monitor and control LLM interaction--have emerged as a promising solution. However, the current landscape of LLM guardrails is fragmented, lacking a unified taxonomy and comprehensive evaluation framework. In this Systematization of Knowledge (SoK) paper, we pre…

A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarks
Junhua Lin (University of Edinburgh), Marc Juarez (University of Edinburgh)
https://arxiv.org/abs/2506.10502

A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarks
We present a novel attack specifically designed against Tree-Ring, a watermarking technique for diffusion models known for its high imperceptibility and robustness against removal attacks. Unlike previous removal attacks, which rely on strong assumptions about attacker capabilities, our attack only requires access to the variational autoencoder that was used to train the target diffusion model, a component that is often publicly available. By leveraging this variational autoencoder, the attacke…

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications
Felix H\"arer
https://arxiv.org/abs/2506.10467 htt…

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications
Recent advancements in LLMs indicate potential for novel applications, e.g., through reasoning capabilities in the latest OpenAI and DeepSeek models. For applying these models in specific domains beyond text generation, LLM-based multi-agent approaches can be utilized that solve complex tasks by combining reasoning techniques, code generation, and software execution. Applications might utilize these capabilities and the knowledge of specialized LLM agents. However, while many evaluations are pe…

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks
Kaiyuan Zhang, Siyuan Cheng, Hanxi Guo, Yuetian Chen, Zian Su, Shengwei An, Yuntao Du, Charles Fleming, Ashish Kundu, Xiangyu Zhang, Ninghui Li
https://arxiv.org/abs/2506.10424

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks
Large language models (LLMs) have achieved remarkable success and are widely adopted for diverse applications. However, fine-tuning these models often involves private or sensitive information, raising critical privacy concerns. In this work, we conduct the first comprehensive study evaluating the vulnerability of fine-tuned LLMs to membership inference attacks (MIAs). Our empirical analysis demonstrates that MIAs exploit the loss reduction during fine-tuning, making them highly effective in re…

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Zhaoxuan Kan, Husheng Han, Shangyi Shi, Tenghui Hua, Hang Lu, Xiaowei Li, Jianan Mu, Xing Hu
https://arxiv.org/abs/2506.10399

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Graph Convolutional Neural Networks (GCNs) have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant. Homomorphic Encryption (HE) facilitates Privacy-Preserving Machine Learning (PPML) by allowing computations to be performed on encrypted data. However, HE introduces substantial computational overhea…

Adaptive Chosen-Ciphertext Security of Distributed Broadcast Encryption
Kwangsu Lee
https://arxiv.org/abs/2506.10338 https://arxiv.or…

Adaptive Chosen-Ciphertext Security of Distributed Broadcast Encryption
Distributed broadcast encryption (DBE) is a specific kind of broadcast encryption (BE) where users independently generate their own public and private keys, and a sender can efficiently create a ciphertext for a subset of users by using the public keys of the subset users. Previously proposed DBE schemes have been proven in the adaptive chosen-plaintext attack (CPA) security model and have the disadvantage of requiring linear number of pairing operations when verifying the public key of a user.…

A Comprehensive Survey of Unmanned Aerial Systems' Risks and Mitigation Strategies
Sharad Shrestha, Mohammed Ababneh, Satyajayant Misra, Henry M. Cathey, Jr., Roopa Vishwanathan, Matt Jansen, Jinhong Choi, Rakesh Bobba, Yeongjin Jang
https://arxiv.org/abs/2506.10327

A Comprehensive Survey of Unmanned Aerial Systems' Risks and Mitigation Strategies
In the last decade, the rapid growth of Unmanned Aircraft Systems (UAS) and Unmanned Aircraft Vehicles (UAV) in communication, defense, and transportation has increased. The application of UAS will continue to increase rapidly. This has led researchers to examine security vulnerabilities in various facets of UAS infrastructure and UAVs, which form a part of the UAS system to reinforce these critical systems. This survey summarizes the cybersecurity vulnerabilities in several phases of UAV deplo…

ELFuzz: Efficient Input Generation via LLM-driven Synthesis Over Fuzzer Space
Chuyang Chen, Brendan Dolan-Gavitt, Zhiqiang Lin
https://arxiv.org/abs/2506.10323

ELFuzz: Efficient Input Generation via LLM-driven Synthesis Over Fuzzer Space
Generation-based fuzzing produces appropriate testing cases according to specifications of input grammars and semantic constraints to test systems and software. However, these specifications require significant manual efforts to construct. This paper proposes a new approach, ELFuzz (Evolution Through Large Language Models for Fuzzing), that automatically synthesizes generation-based fuzzers tailored to a system under test (SUT) via LLM-driven synthesis over fuzzer space. At a high level, it sta…

Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods
Yeonwoo Jang, Shariqah Hossain, Ashwin Sreevatsa, Diogo Cruz
https://arxiv.org/abs/2506.10236

Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods
In this work, we show that some machine unlearning methods may fail when subjected to straightforward prompt attacks. We systematically evaluate eight unlearning techniques across three model families, and employ output-based, logit-based, and probe analysis to determine to what extent supposedly unlearned knowledge can be retrieved. While methods like RMU and TAR demonstrate robust unlearning, ELM remains vulnerable to specific prompt attacks (e.g., Hindi filler text in original prompt recover…

Guardians of the Regime: When and Why Autocrats Create Secret Police
Marius Mehrl, Mila Pfander, Theresa Winner, Cornelius Fritz
https://arxiv.org/abs/2506.10194

Guardians of the Regime: When and Why Autocrats Create Secret Police
Autocrats use secret police to stay in power, as these organizations deter and suppress opposition to their rule. Existing research shows that secret police are very good at this but, surprisingly, also that they are not as ubiquitous in autocracies as one may assume, existing in less than 50% of autocratic country-years. We thus explore under which conditions secret police emerge in dictatorships. For this purpose, we apply statistical variable selection techniques to identify which of several…

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Nanda Rani, Sandeep Kumar Shukla
https://arxiv.org/abs/2506.10175

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Effective attribution of Advanced Persistent Threats (APTs) increasingly hinges on the ability to correlate behavioral patterns and reason over complex, varied threat intelligence artifacts. We present AURA (Attribution Using Retrieval-Augmented Agents), a multi-agent, knowledge-enhanced framework for automated and interpretable APT attribution. AURA ingests diverse threat data including Tactics, Techniques, and Procedures (TTPs), Indicators of Compromise (IoCs), malware details, adversarial to…

Disclosure Audits for LLM Agents
Saswat Das, Jameson Sandler, Ferdinando Fioretto
https://arxiv.org/abs/2506.10171 https://arxiv.org/…

Disclosure Audits for LLM Agents
Large Language Model agents have begun to appear as personal assistants, customer service bots, and clinical aides. While these applications deliver substantial operational benefits, they also require continuous access to sensitive data, which increases the likelihood of unauthorized disclosures. This study proposes an auditing framework for conversational privacy that quantifies and audits these risks. The proposed Conversational Manipulation for Privacy Leakage (CMPL) framework, is an iterati…

Unconditionally Secure Wireless-Wired Ground-Satellite-Ground Communication Networks Utilizing Classical and Quantum Noise
Lucas Truax, Sandip Roy, Laszlo B. Kish
https://arxiv.org/abs/2506.10147

Unconditionally Secure Wireless-Wired Ground-Satellite-Ground Communication Networks Utilizing Classical and Quantum Noise
In this paper, we introduce the Kirchhoff-Law-Johnson-Noise (KLJN) as an approach to securing satellite communications. KLJN has the potential to revolutionize satellite communication security through its combination of simplicity, cost-effectiveness, and resilience with unconditional security. Unlike quantum key distribution (QKD), which requires complex, fragile, and expensive infrastructure like photon detectors and dedicated optical links, KLJN operates using standard electronic components …

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning
Muqi Zou (Jing), Hongyu Cai (Jing), Hongwei Wu (Jing), Zion Leonahenahe Basque (Jing), Arslan Khan (Jing), Berkay Celik (Jing), Dave (Jing), Tian (Fish), Antonio Bianchi (Fish), Ruoyu (Fish), Wang, Dongyan Xu
https://arxiv.org/abs/2506.10125…

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning
Decompilers, which reconstruct human-readable source code from binary executables, are vital to many security tasks. Yet, despite recent advances, their output often suffers from syntactic and semantic errors and remains difficult to read. Recently, with the advent of large language models (LLMs), researchers began to explore the potential of LLMs to refine decompiler output. Nevertheless, our study of these approaches reveals significant limitations, such as introducing new errors and relying …

Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
David Farr, Kevin Talty, Alexandra Farr, John Stockdale, Iain Cruickshank, Jevin West
https://arxiv.org/abs/2506.10104

Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
As cyber threats become more sophisticated, rapid and accurate vulnerability detection is essential for maintaining secure systems. This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weakness Enumerations (CWEs), comparing zero-shot, few-shot cross-domain, and few-shot in-domain prompting strategies. Our results indicate that while zero-shot prompting performs poorly, few-shot prompti…

GenBreak: Red Teaming Text-to-Image Generators Using Large Language Models
Zilong Wang, Xiang Zheng, Xiaosen Wang, Bo Wang, Xingjun Ma, Yu-Gang Jiang
https://arxiv.org/abs/2506.10047

GenBreak: Red Teaming Text-to-Image Generators Using Large Language Models
Text-to-image (T2I) models such as Stable Diffusion have advanced rapidly and are now widely used in content creation. However, these models can be misused to generate harmful content, including nudity or violence, posing significant safety risks. While most platforms employ content moderation systems, underlying vulnerabilities can still be exploited by determined adversaries. Recent research on red-teaming and adversarial attacks against T2I models has notable limitations: some studies succes…

Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions
Ece Gumusel
https://arxiv.org/abs/2506.10042 https://a…

Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions
In an era of increasing interaction with artificial intelligence (AI), users face evolving privacy decisions shaped by complex, uncertain factors. This paper introduces Multiverse Privacy Theory, a novel framework in which each privacy decision spawns a parallel universe, representing a distinct potential outcome based on user choices over time. By simulating these universes, this theory provides a foundation for understanding privacy through the lens of contextual integrity, evolving preferenc…

Symbolic Generation and Modular Embedding of High-Quality abc-Triples
Michael A. Idowu
https://arxiv.org/abs/2506.10039 https://arxiv…

Symbolic Generation and Modular Embedding of High-Quality abc-Triples
We present a symbolic identity for generating integer triples $(a, b, c)$ satisfying $a + b = c$, inspired by structural features of the \emph{abc conjecture}. The construction uses powers of $2$ and $3$ in combination with modular inversion in $\mathbb{Z}/3^p\mathbb{Z}$, leading to a parametric identity with residue constraints that yield abc-triples exhibiting low radical values. Through affine transformations, these symbolic triples are embedded into a broader space of high-quality examples,…

Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service Environment
Tianyu Chen, Jian Lou, Wenjie Wang
https://arxiv.org/abs/2506.10030 https:/…

Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service Environment
As Retrieval-Augmented Generation (RAG) evolves into service-oriented platforms (Rag-as-a-Service) with shared knowledge bases, protecting the copyright of contributed data becomes essential. Existing watermarking methods in RAG focus solely on textual knowledge, leaving image knowledge unprotected. In this work, we propose AQUA, the first watermark framework for image knowledge protection in Multimodal RAG systems. AQUA embeds semantic signals into synthetic images using two complementary meth…

Evaluation empirique de la s\'ecurisation et de l'alignement de ChatGPT et Gemini: analyse comparative des vuln\'erabilit\'es par exp\'erimentations de jailbreaks
Rafa\"el Nouailles (GdR)
https://arxiv.org/abs/2506.10029

Evaluation empirique de la sécurisation et de l'alignement de ChatGPT et Gemini: analyse comparative des vulnérabilités par expérimentations de jailbreaks
Large Language models (LLMs) are transforming digital usage, particularly in text generation, image creation, information retrieval and code development. ChatGPT, launched by OpenAI in November 2022, quickly became a reference, prompting the emergence of competitors such as Google's Gemini. However, these technological advances raise new cybersecurity challenges, including prompt injection attacks, the circumvention of regulatory measures (jailbreaking), the spread of misinformation (hallucinat…

Secure Data Access in Cloud Environments Using Quantum Cryptography
S. Vasavi Venkata Lakshmi, Ziaul Haque Choudhury
https://arxiv.org/abs/2506.10028 https…

Secure Data Access in Cloud Environments Using Quantum Cryptography
Cloud computing has made storing and accessing data easier but keeping it secure is a big challenge nowadays. Traditional methods of ensuring data may not be strong enough in the future when powerful quantum computers become available. To solve this problem, this study uses quantum cryptography to protect data in the cloud environment. Quantum Key Distribution (QKD) creates secure keys by sending information using quantum particles like photons. Specifically, we use the BB84 protocol, a simple …

Mind the Gap: Revealing Security Barriers through Situational Awareness of Small and Medium Business Key Decision-Makers
Yuanhaur Chang, Oren Heller, Yaniv Shlomo, Iddo Bar-Noy, Ella Bokobza, Michal Grinstein-Weiss, Ning Zhang
https://arxiv.org/abs/2506.10025

Mind the Gap: Revealing Security Barriers through Situational Awareness of Small and Medium Business Key Decision-Makers
Key decision-makers in small and medium businesses (SMBs) often lack the awareness and knowledge to implement cybersecurity measures effectively. To gain a deeper understanding of how SMB executives navigate cybersecurity decision-making, we deployed a mixed-method approach, conducting semi-structured interviews (n=21) and online surveys (n=322) with SMB key decision-makers. Using thematic analysis, we revealed SMB decision-makers' perceived risks in terms of the digital assets they valued, and…

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models
Elena Sofia Ruzzetti, Giancarlo A. Xompero, Davide Venditti, Fabio Massimo Zanzotto
https://arxiv.org/abs/2506.10024

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models
Large Language Models (LLMs) memorize, and thus, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information (PII), which should not be stored and, consequently, not leaked. In this paper, we introduce Private Memorization Editing (PME), an approach for preventing private data leakage that turns an apparent limitation, that is, the LLMs' memorization ability, into a powerful privacy defense strategy. While attacks against LLMs have been performed exploiting previou…

LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges
Haoyang Li, Huan Gao, Zhiyuan Zhao, Zhiyu Lin, Junyu Gao, Xuelong Li
https://arxiv.org/abs/2506.10022

LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges
The widespread adoption of Large Language Models (LLMs) has heightened concerns about their security, particularly their vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs. While prior research has been conducted on general security capabilities of LLMs, their specific susceptibility to jailbreak attacks in code generation remains largely unexplored. To fill this gap, we propose MalwareBench, a benchmark dataset containing 3,520 jailbreaking prompts f…

From Threat to Tool: Leveraging Refusal-Aware Injection Attacks for Safety Alignment
Kyubyung Chae, Hyunbin Jin, Taesup Kim
https://arxiv.org/abs/2506.10020

From Threat to Tool: Leveraging Refusal-Aware Injection Attacks for Safety Alignment
Safely aligning large language models (LLMs) often demands extensive human-labeled preference data, a process that's both costly and time-consuming. While synthetic data offers a promising alternative, current methods frequently rely on complex iterative prompting or auxiliary models. To address this, we introduce Refusal-Aware Adaptive Injection (RAAI), a straightforward, training-free, and model-agnostic framework that repurposes LLM attack techniques. RAAI works by detecting internal refusal…