cyberintel.kalymoon.com · 5269 articles · updated every 4 hours · grows forever
arXiv:2603.12946v1 Announce Type: new Abstract: Privacy-Preserving Machine Learning as a Service (PP-MLaaS) enables secure neural network inference by integrating cryptographic primitives such as homo…
arXiv:2603.12968v1 Announce Type: new Abstract: Authentication is crucial to confirm that an individual or entity trying to perform an action is actually who or what they claim to be. In dynamic envir…
arXiv:2603.12990v1 Announce Type: new Abstract: Cryptocurrency exchanges use proofs of liabilities (PoLs) to prove to their customers their liabilities committed on-chain, thereby enhancing their trus…
arXiv:2603.13014v1 Announce Type: new Abstract: The proposed method (FraudFox) provides solutions to adversarial attacks in a resource constrained environment. We focus on questions like the following…
arXiv:2603.13028v1 Announce Type: new Abstract: Diffusion models enable high-fidelity image editing but can also be misused for unauthorized style imitation and harmful content generation. To mitigate…
arXiv:2603.13151v1 Announce Type: new Abstract: OpenClaw-like agents offer substantial productivity benefits, yet they are insecure by default because they combine untrusted inputs, autonomous action,…
arXiv:2603.13181v1 Announce Type: new Abstract: Existing methods for verifying access control policies require the policy to be complete and fully determined before verification can proceed, but in pr…
arXiv:2603.12277v1 Announce Type: cross Abstract: Language models remain vulnerable to prompt injection attacks despite extensive safety training. We trace this failure to role confusion: models infer…
arXiv:2603.12414v1 Announce Type: cross Abstract: State Space Models (SSMs) such as Mamba achieve linear-time sequence processing through input-dependent recurrence, but this mechanism introduces a cr…
arXiv:2603.12435v1 Announce Type: cross Abstract: State-of-the-art DRAM read disturbance mitigations rely on the read disturbance threshold (RDT) (e.g., the number of aggressor row activations needed …
arXiv:2603.12582v1 Announce Type: cross Abstract: Textual adversarial attacks pose a serious security threat to Natural Language Processing (NLP) systems by introducing imperceptible perturbations tha…
arXiv:2603.12614v1 Announce Type: cross Abstract: Tool-augmented LLM agents increasingly rely on multi-step, multi-tool workflows to complete real tasks. This design expands the attack surface, becaus…
arXiv:2603.12749v1 Announce Type: cross Abstract: Watermarking the initial noise of diffusion models has emerged as a promising approach for image provenance, but content-independent noise patterns ca…
arXiv:2603.12753v1 Announce Type: cross Abstract: Absolute anonymization, conceived as an irreversible transformation that prevents re-identification and sensitive value disclosure, has proven to be a…
arXiv:2603.12949v1 Announce Type: cross Abstract: Robust invisible watermarks are widely used to support copyright protection, content provenance, and accountability by embedding hidden signals design…
arXiv:2603.12989v1 Announce Type: cross Abstract: Despite the strong multimodal performance, large vision-language models (LVLMs) are vulnerable during fine-tuning to backdoor attacks, where adversari…
arXiv:2603.13026v1 Announce Type: cross Abstract: Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been propose…
arXiv:2603.13186v1 Announce Type: cross Abstract: Prior approaches for membership privacy preservation usually update or retrain all weights in neural networks, which is costly and can lead to unneces…
arXiv:2410.16326v3 Announce Type: replace Abstract: The fidelity and utility of synthetic network traffic are critically compromised by architectural mismatch across heterogeneous network datasets and…
arXiv:2603.12287v1 Announce Type: new Abstract: We address the problem of transforming raw vessel trajectory data collected from AIS into structured and semantically enriched representations interpret…
arXiv:2603.12372v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational s…
arXiv:2603.12483v1 Announce Type: new Abstract: Across many domains (e.g., IoT, observability, telecommunications, cybersecurity), there is an emerging adoption of conversational data analysis agents …
arXiv:2603.12710v1 Announce Type: new Abstract: Developing autonomous agents for web-based tasks is a core challenge in AI. While Large Language Model (LLM) agents can interpret complex user requests,…
arXiv:2603.12733v1 Announce Type: new Abstract: Catastrophic failures of marine engines imply severe loss of functionality and destroy or damage the systems irreversibly. Being sudden and often unpred…