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◬ AI & Machine Learning Aug 14, 2026
CertiK at IDAI Summit 2026: AI Adoption & Digital Asset Cybersecurity - CertiK

CertiK at IDAI Summit 2026: AI Adoption & Digital Asset Cybersecurity CertiK

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◬ AI & Machine Learning Aug 14, 2026
Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice

arXiv:2608.12962v1 Announce Type: cross Abstract: Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models. In this set…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Technical Report on Resilient and Secure Large-Scale Energy Internet Systems

arXiv:2608.12916v1 Announce Type: cross Abstract: This IEEE PES Task Force report examines the security and resilience of large-scale Energy Internet (EI) systems, in which electricity, information, a…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Beyond Visual Evidence: Revealing and Mitigating Relational Privacy Leakage in Document MLLMs

arXiv:2608.12911v1 Announce Type: cross Abstract: While the privacy risks of multimodal large language models (MLLMs) have drawn significant attention, the unique vulnerabilities of domain-specific ML…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Dissecting Software Graphs: Structural Insights for Driver-Guided Fuzzing

arXiv:2608.12859v1 Announce Type: cross Abstract: Many software systems expose multiple execution modes through command-line options, subcommands, and configuration flags. For such programs, fuzzing d…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Correct Is Not Governed: Provenance Integrity in Agentic Workflows

arXiv:2608.12761v1 Announce Type: cross Abstract: Agentic workflows are commonly evaluated by whether they reach the correct outcome. That is insufficient in institutional settings, where a correct ac…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs

arXiv:2608.12675v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries. Existing pri…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
TeleGapper: On the (un)reliability of Privacy Policies in Telegram Mini apps

arXiv:2608.13390v1 Announce Type: new Abstract: Telegram Mini Apps are Web applications embedded within the Telegram client, forming an ecosystem of third-party services within one of the world's most…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
VR-Themis: A Scalable Framework for Virtual Reality Application Clone Detection

arXiv:2608.13290v1 Announce Type: new Abstract: Repackaging of mobile applications (aka app cloning) not only threatens the security and privacy of mobile users but also infringes upon the copyright o…

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◬ AI & Machine Learning Aug 14, 2026
Slow and Steady: Preventing MEV with Verifiable Delays

arXiv:2608.13271v1 Announce Type: new Abstract: Our work presents a defense mechanism against Maximal Extractable Value (MEV) opportunities in distributed ledgers. The mechanism relies on the idea of …

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Smart Contract Invariants Protect Against Cybercriminals

arXiv:2608.13191v1 Announce Type: new Abstract: Blockchains are among the most adversarial environments in computing. Billions are stolen by cybercriminals who exploit vulnerabilities. This is an open…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
A Commitment-Based Hybrid Post-Quantum Cryptographic Model for Multi-File Cloud Storage

arXiv:2608.13138v1 Announce Type: new Abstract: Cloud storage clients increasingly require authentication that remains secure against future quantum-capable adversaries, motivating hybrid construction…

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◬ AI & Machine Learning Aug 14, 2026
Operationalizing Cyber Threat Intelligence with GraphRAG

arXiv:2608.13050v1 Announce Type: new Abstract: When a security researcher publishes a report on a cyberattack, detection engineers are supposed to turn it into working detection rules. In practice, m…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
InSPECtor: Improving SLEIGH Processor Specification Veracity via Proxy

arXiv:2608.13042v1 Announce Type: new Abstract: Processor specifications underpin critical security and program- analysis tools such as disassemblers, decompilers, and emulators, yet, their correctnes…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
InterSAGE: The Secure and Verifiable Interoperability Protocol for An Internet of Agents

arXiv:2608.13030v1 Announce Type: new Abstract: The emerging Internet of Agents enables LLM-powered agents to discover peers, invoke tools, and delegate tasks across organizational boundaries. Existin…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
OmniSphinx: Active Mix Networks (Extended Version)

arXiv:2608.13008v1 Announce Type: new Abstract: Mix networks are an important tool to implement anonymous communication, which protects not just the content but also the metadata of messages. Over tim…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
ATOBench: Tracing How Autonomous Penetration-Testing Agents Verify Vulnerabilities When Target Evidence Lies

arXiv:2608.12996v1 Announce Type: new Abstract: Autonomous penetration-testing agents rely on target responses. These responses guide both subsequent actions and the final report. A deceptive response…

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◬ AI & Machine Learning Aug 14, 2026
Beyond Handcrafted Security: Towards Self-Evolving Defense for LLM Agents

arXiv:2608.12977v1 Announce Type: new Abstract: The expanding operational capabilities of large language model (LLM) agents introduce sophisticated security threats. Runtime defenses have emerged as a…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Adversarial Robustness in Smishing Detection: A Comparative Analysis of Adversarial Fragility in Classical vs. Transformer-Based Detection Systems

arXiv:2608.12889v1 Announce Type: new Abstract: Smishing detection systems are commonly trained and evaluated on clean, monolingual text. In low-resource settings, however, attackers frequently circum…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Labels Are Not Endpoints: Treatment Leakage and Construct Validity in MCP Agent Security Evaluation

arXiv:2608.12880v1 Announce Type: new Abstract: Security evaluations of tool-using agents often equate stored labels with behavioral facts. We audit a preserved campaign by tracing 10,200 execution ro…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Discovering Persistent Behavioural Patterns for Interpretable Blockchain Forensics

arXiv:2608.12864v1 Announce Type: new Abstract: Public blockchain data enables large-scale DeFi-related analysis, but many existing approaches are application-specific, difficult to scale, or hard to …

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
Beyond Source: An Empirical Study of Python Bytecode Security Risks

arXiv:2608.12853v1 Announce Type: new Abstract: Python package security is largely source-centric, yet Python runtimes can execute bytecode directly through .pyc files, compiled-only modules, and mars…

arXiv Security Read →
◬ AI & Machine Learning Aug 14, 2026
RealmEye: Virtual Machine Introspection for Arm CCA Realm VMs

arXiv:2608.12822v1 Announce Type: new Abstract: Confidential VMs (CVMs) have become the dominant substrate for sensitive cloud workloads, from financial services to privacy-preserving AI inference. Th…

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◬ AI & Machine Learning Aug 14, 2026
PIPES: Securing Agent Perception with Provenance and Priors

arXiv:2608.12789v1 Announce Type: new Abstract: Tool-using agents consume external data from sources with different levels of trust, yet tool responses rarely identify who produced each component or w…

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