309 papers
This paper provides a comprehensive comparative analysis of five leading cross-chain token standards—xERC20, OFT, NTT, CCT, and SuperchainERC20—examining their architectural designs, message-passing mechanisms, and security features to highlight their distinct implementation approaches, trust models, and target ecosystems despite their shared goal of seamless cross-chain fungibility.
This paper introduces SIABENCH, a novel agentic evaluation framework featuring a comprehensive dataset and autonomous agent to rigorously benchmark the effectiveness of 11 major LLMs across diverse security incident analysis tasks, addressing the critical lack of standardized assessment tools in the field.
Proteus is a practical, low-overhead framework for Android device logs that ensures privacy-preserving forensic analysis by combining keyed-hash pseudonymization with time-rotating encryption to prevent PII exposure and multi-snapshot correlation while maintaining data fidelity.
This paper identifies and exploits a novel radio-frequency side-channel attack on trapped-ion quantum computers by detecting leaked signals from acousto-optical modulators to extract proprietary gate characteristics, while also proposing mitigation strategies.
This paper introduces a novel differentially private framework for estimating cumulative distribution functions using polynomial projection and sparse matching pursuit methods, which offer superior performance and adaptability for decentralized and streaming data scenarios compared to existing approaches.
The paper presents SHIELD, a host-independent framework that leverages deep filesystem features parsed at the storage controller level to achieve tamper-resistant, real-time ransomware detection and mitigation with over 97% accuracy and minimal impact on benign applications.
This paper proposes the Constrained Rényi Entropy Optimization (CREO) framework, a backward-compatible mathematical approach that enhances RSA's resistance to Shor's algorithm by constraining prime proximity to increase the quantum resource requirements for factorization, thereby offering a transitional security upgrade without necessitating infrastructure replacement.
This paper introduces the framework of "refereed learning," where a learner accesses two competing provers (one honest, one dishonest) to select the superior of two black-box models with high precision using only a single ground truth query and minimal communication, achieving accuracy far beyond what is possible with a single prover or without provers.
This paper demonstrates that expert routing decisions in Mixture-of-Experts (MoE) language models leak substantial information, enabling advanced machine learning attacks to reconstruct up to 91.2% of original text tokens from routing data alone, thereby establishing expert selections as sensitive information comparable to the underlying text.
This paper presents a comprehensive survey and categorization of state-of-the-art quantitative trust models for online social networks, bridging psychological theories with algorithmic foundations to provide a practical handbook for implementation while highlighting unresolved challenges.
This paper proposes a three-stage framework that filters communication-layer defects from AIS data to reliably detect and classify wide-area GNSS spoofing and jamming events, achieving a 98.6% reduction in false alarms compared to naive clustering methods.
This paper presents the first comprehensive survey and systematic framework for understanding the security landscape of agentic AI, analyzing its design space, attack vectors, and defense mechanisms while identifying critical gaps and open challenges in securing these emerging systems.
The paper introduces WebWeaver, a stealthy attack framework that infers the complete communication topology of LLM-based multi-agent systems by compromising only a single arbitrary agent and leveraging context-based diffusion mechanisms, thereby overcoming the limitations of prior methods that rely on impractical assumptions or easily defeated jailbreaks.
This paper introduces a systematic scaling-law framework to analyze how jailbreak attack success scales with computational effort across diverse methods and models, revealing that prompting-based approaches are significantly more compute-efficient and stealthy than optimization-based methods while vulnerability varies strongly by harm type.
This paper introduces DNS-GT, a novel self-supervised Transformer-based model that learns contextual domain name embeddings from DNS query sequences to outperform existing baselines in tasks like domain classification and botnet detection.
This paper proposes Secure Concept Steering for CodeLLMs (SCS-Code), a novel mechanism that leverages the internal representations of security concepts within Large Language Models to actively steer token generation toward secure and functional code, thereby outperforming existing state-of-the-art methods in addressing security vulnerabilities.
This paper proposes a multi-agent reinforcement learning framework to compute a Nash equilibrium in a zero-sum game between an attacker and a defender, effectively detecting false data injection attacks in vehicular routing to ensure network resilience and bounded travel times.
This paper presents the first construction of reusable, unclonable encryption for arbitrary-length messages in the Haar random oracle model, demonstrating that such schemes can exist even in the absence of one-way functions by leveraging a new unitary reprogramming lemma.
This paper introduces KEPo, a novel poisoning attack method specifically designed to exploit the graph-based retrieval mechanism of GraphRAG systems by fabricating toxic knowledge evolution paths that manipulate the knowledge graph structure to force Large Language Models into generating harmful responses, thereby achieving state-of-the-art attack success rates where conventional RAG attacks fail.
This paper establishes the strict optimality of frequency estimation under local differential privacy by proving that a symmetric, extremal estimator with an optimized constant support size achieves maximum precision and minimal communication cost, while introducing a modified Count-Mean Sketch that practically attains this theoretical bound.