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◬ AI & Machine Learning May 20, 2026
Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production

arXiv:2605.18818v1 Announce Type: new Abstract: Academic research tends to focus on new models for document understanding creating a wide gap in the literature between model definition and running mod…

arXiv AI Read →
◬ AI & Machine Learning May 20, 2026
Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance

arXiv:2605.18801v1 Announce Type: new Abstract: Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, i…

arXiv AI Read →
◬ AI & Machine Learning May 20, 2026
Awakening the Hydra: Stabilizing Multi-Concept Backdoor Injection in Text-to-Image Diffusion Models

arXiv:2605.19698v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly developed through open-source reuse and repeated downstream fine-tuning, where reused checkpoints are di…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
SCARA: A Semantics-Constrained Autonomous Remediation Agent for Opaque Industrial Software Vulnerabilities

arXiv:2605.19668v1 Announce Type: new Abstract: Critical-infrastructure operators are increasingly expected to assess and remediate vulnerabilities in deployed industrial software. However, much of th…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Inferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategies

arXiv:2605.19644v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are a powerful representation of linked data, offering flexibility, semantic richness, and support for knowledge enrichment and r…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures

arXiv:2605.19478v1 Announce Type: new Abstract: Existing ViT backdoor attacks based on backbone-overwriting full-tuning are computationally expensive and inflict performance degradation. This has forc…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
XAI FL-IDS: A Federated Learning and SHAP-Based Explainable Framework for Distributed Intrusion Detection Systems

arXiv:2605.19448v1 Announce Type: new Abstract: An Intrusion Detection System (IDS) is vital in cybersecurity, detecting unauthorized activity across networks. With attacks on network layers increasin…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
High-Rate Public-Key Pseudorandom Codes for Edit Errors

arXiv:2605.19402v1 Announce Type: new Abstract: Pseudorandom codes (PRCs), introduced by Christ and Gunn (CRYPTO '2024), are error-correcting codes whose codewords are computationally indistinguishabl…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Locked Out at 8,000 Miles: Why UK-China Partnership Students Are Suffering

arXiv:2605.19367v1 Announce Type: new Abstract: University cybersecurity protocols have intensified dramatically in response to rising threats of data breaches, ransomware, and credential theft. While…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
RoboJailBench: Benchmarking Adversarial Attacks and Defenses in Embodied Robotic Agents

arXiv:2605.19328v1 Announce Type: new Abstract: Recent advances in Vision-Language Models (VLMs) facilitate a new class of embodied AI systems, where these models are integrated into physical platform…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Exploring and Developing a Pre-Model Safeguard with Draft Models

arXiv:2605.19321v1 Announce Type: new Abstract: Large Language Model (LLM) alignment remains vulnerable to jailbreak attacks that elicit unsafe responses, motivating pre-model and post-model guards. P…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
MultiBallot: Verifiable and privacy-preserving E-Collecting in the Swiss setting

arXiv:2605.19312v1 Announce Type: new Abstract: As part of the political process, citizens may participate in signature collections to influence policy changes. In Switzerland, this even results in le…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Detecting and Mitigating Backdoor Attacks in OTA-FL Systems: A Two-Stage Robust Aggregation Scheme

arXiv:2605.19253v1 Announce Type: new Abstract: Over-the-air federated learning (OTA-FL) improves communication efficiency by exploiting the superposition property of wireless channels, but this same …

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets

arXiv:2605.19233v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS o…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Devilray: A Systematic Adversarial Model Revealing Blind Spots in Fake Base Station Detection

arXiv:2605.19232v1 Announce Type: new Abstract: Fake Base Station (FBS) detection has been a critical focus of cellular security research for over two decades. However, significant financial and regul…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models

arXiv:2605.19227v1 Announce Type: new Abstract: Unified autoregressive models (UAMs) are transformer models that generate text as well as image tokens within a single autoregressive pass. Shared param…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
On the Geometric Limits of Transformer Defenses against Obfuscation Attacks: Latent Embedding Collapse & Performance Robustness Gap

arXiv:2605.19159v1 Announce Type: new Abstract: Prompt injection attacks pose significant risks to language model safety, yet existing defenses are typically evaluated using classification performance…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks

arXiv:2605.19147v1 Announce Type: new Abstract: Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samples are poisoned using trigger-based harmful content…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Structural Analysis of Cryptographic Sequences using Stringology-Based Fingerprinting

arXiv:2605.19123v1 Announce Type: new Abstract: Cryptographic primitives such as stream ciphers,Pseudorandom Number Generators (PRNGs), and block cipher modes produce sequences that are designed to be…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Agent Security is a Systems Problem

arXiv:2605.18991v1 Announce Type: new Abstract: We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted compon…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
Surviving the Unseen: Predictive Defense for Novel Multi-Turn Multimodal Attacks

arXiv:2605.18988v1 Announce Type: new Abstract: The expansion of Multimodal Large Language Models (MLLMs) and their integration into autonomous agentic workflows has introduced a non-stationary attack…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
OEP: Poisoning Self-Evolving LLM Agents via Locally Correct but Non-Transferable Experiences

arXiv:2605.18930v1 Announce Type: new Abstract: Memory-augmented large language model (LLM) agents use iterative reflection and self-evolution to solve complex tasks, but these mechanisms introduce se…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks

arXiv:2605.18919v1 Announce Type: new Abstract: Evolutionary algorithms for adversarial attacks leverage population-based search to discover perturbations without gradient information, but suffer from…

arXiv Security Read →
◬ AI & Machine Learning May 20, 2026
ESLD (External Surrogate Latent Defense): A Latent-Space Architecture for Faster, Stronger Prompt-Injection Defense

arXiv:2605.18918v1 Announce Type: new Abstract: Modern AI assistants are agentic. To answer a single user request, the underlying language model pulls in information from many sources, such as web sea…

arXiv Security Read →
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