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◬ AI & Machine Learning Jun 18, 2026
Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

arXiv:2606.19129v1 Announce Type: new Abstract: Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learnin…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Quantifying Compromise Risk in Exceptional Access Architectures Under Sparse and Indirect Evidence

arXiv:2606.19106v1 Announce Type: new Abstract: Lawful exceptional access (EA) systems hold the cryptographic keys that decrypt protected communications for authorised parties. The debate over their r…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Compute-Budgeted Exploitability Evidence Graphs for Prospective Vulnerability Triage

arXiv:2606.19076v1 Announce Type: new Abstract: Defenders cannot patch every newly disclosed vulnerability at once, so exploitability prediction must be evaluated prospectively rather than retrospecti…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
PYPILINE: Malicious PyPI Package Detection via Suspicious API Knowledge and Agent Workflow

arXiv:2606.19063v1 Announce Type: new Abstract: The detection of malicious PyPI packages is crucial for maintaining the security of the open source software supply chain. Existing methods, which prima…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution

arXiv:2606.19023v1 Announce Type: new Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious b…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
TRAP: Benchmark for Task-completion and Resistance to Active Privacy-extraction

arXiv:2606.18996v1 Announce Type: new Abstract: Agents are increasingly deployed in document-intensive workflows where sensitive private information is not an edge case but a routine input, e.g., an a…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
A Predictive Neural Network Architecture for Early Detection of Low-Rate Cyberattacks

arXiv:2606.18771v1 Announce Type: new Abstract: Low-Rate Denial of Service (LDoS) attacks pose a significant challenge to IoT networks due to their subtle and prolonged nature, often evading tradition…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Image Prompt Reconstruction Attacks on Distributed MLLM Inference Frameworks

arXiv:2606.18710v1 Announce Type: new Abstract: Distributed large language model (LLM) inference frameworks connect isolated consumer-grade devices for large-scale model inference, substantially reduc…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications

arXiv:2606.18673v1 Announce Type: new Abstract: Large language model (LLM)-based applications rely on system prompts to encode core logic and developer-defined constraints, making these prompts import…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
TGCM: Topic-Guided Generative Disentanglement of Interleaved APT Technique Sequences

arXiv:2606.18651v1 Announce Type: new Abstract: In enterprise environments, multiple Advanced Persistent Threat (APT) campaigns often unfold concurrently, producing audit logs in which attack techniqu…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Code-Augur: Agentic Vulnerability Detection via Specification Inference

arXiv:2606.18619v1 Announce Type: new Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security. Audits conducted entirely by autonomous LLM …

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
MIDS: Detecting Stealthy Masquerade and Tampering Attacks on CAN Bus via Bidirectional Mamba

arXiv:2606.18599v1 Announce Type: new Abstract: The Controller Area Network (CAN) protocol is the primary communication standard for Electronic Control Units (ECUs) in modern vehicles, but its lack of…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
The Gate Is Only as Honest as Its Contracts: ContractGuard for the Contract Layer of Risk-Aware Causal Gating

arXiv:2606.18550v1 Announce Type: new Abstract: Risk-Aware Causal Gating (RACG) defends tool-augmented LLM agents against indirect prompt injection by removing dangerous tools from the agent's visible…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Confident yet Concerned: Inconsistencies in Computing Students' Attitudes on Cybersecurity

arXiv:2606.18541v1 Announce Type: new Abstract: Today's young adults are most immersed in technology, leading in feelings of powerlessness in managing online privacy across many platforms, and particu…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework

arXiv:2606.18532v1 Announce Type: new Abstract: AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture. Fo…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Evaluating Prompting-Based Defenses Against Domain-Camouflaged Injection Attacks

arXiv:2606.18530v1 Announce Type: new Abstract: Domain-camouflaged injection attacks embed malicious instructions in retrieved content using domain-appropriate vocabulary, evading standard detectors t…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
From Bits to Mixed-Radix Keys: Horner Decomposition, Uniform Sampling, and the Information-Theoretic QKD Interface of the MR-OTP

arXiv:2606.18526v1 Announce Type: new Abstract: The Mixed-Radix One-Time Pad (MR-OTP) extends the classical OTP to heterogeneous alphabets while preserving perfect secrecy. We provide a practical, bia…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases

arXiv:2606.18497v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) allows large language models to access external and private corpora for factual, domain-specific responses. Modern …

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Understanding the "Airport" Censorship Circumvention Ecosystem in China

arXiv:2606.18427v1 Announce Type: new Abstract: In China, a burgeoning underground market sells citizens subscription-based censorship circumvention proxies known as ''airports''. We present the first…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Evaluating the Effectiveness of LLMs in Aiding Compliance Testing of PKCS#1-v1.5

arXiv:2606.18405v1 Announce Type: new Abstract: Testing implementations of binary protocols for specification compliance requires inputs that satisfy both structural and semantic constraints. Purely r…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
SafeClawBench: Separating Semantic, Audit-Evidence, and Sandbox Harm in Tool-Using LLM Agents

arXiv:2606.18356v1 Announce Type: new Abstract: Tool-using language-model agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent memory, s…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
Agentra: A Supervisable Multi-Agent Framework for Enterprise Intrusion Response

arXiv:2606.18325v1 Announce Type: new Abstract: Enterprise intrusion response still depends on static playbooks and analyst-driven triage, creating delay between alert generation and containment. We p…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
TopVenues: A Reproducible Corpus and Tooling Substrate for Cybersecurity Literature Reviews

arXiv:2606.18320v1 Announce Type: new Abstract: Cybersecurity literature reviews require a reproducible denominator: the set of papers that a protocol includes before screening and synthesis begin. To…

arXiv Security Read →
◬ AI & Machine Learning Jun 18, 2026
TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization

arXiv:2606.18312v1 Announce Type: new Abstract: Federated learning allows multiple clients to jointly train a shared model by sending gradient updates to a central server while keeping raw inputs loca…

arXiv Security Read →
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