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Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks CTFusion: A CTF-based Benchmark for LLM Agent Evaluation Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World Red-Teaming Agent Execution Contexts: Open-World Security Evaluation on OpenClaw Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs Containment Verification: AI Safety Guarantees Independent of Alignment Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents From Specification to Deployment: Empirical Evidence from a W3C VC + DID Trust Infrastructure for Autonomous Agents Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems When Alignment Isn't Enough: Response-Path Attacks on LLM Agents Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation Chimera: Neuro-Symbolic Attention Primitives for Trustworthy Dataplane Intelligence Sockpuppetting: Jailbreaking LLMs by Combining Prefilling with Optimization StegoStylo: Squelching Stylometric Scrutiny through Steganographic Stitching Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks Feedback Lunch: Learned Feedback Codes for Secure Communications Noise Aggregation Analysis Driven by Small-Noise Injection: Efficient Membership Inference for Diffusion Models A First Look at the Security Issues in the Model Context Protocol Ecosystem Formalizing the Safety, Security, and Functional Properties of Agentic AI Systems MEASER: Malware embedding attacks on open-source LLMs Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG) From surveillance to signalling: escalation channels as environmental controls for agentic AI STAC: When Innocent Tools Form Dangerous Chains to Jailbreak LLM Agents Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids Guidance Watermarking for Diffusion Models SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities Tell-Tale Watermarks for Explanatory Reasoning in Synthetic Media Forensics Between a Rock and a Hard Place: The Tension Between Ethical Reasoning and Safety Alignment in LLMs A Comprehensive Guide to Differential Privacy: From Theory to User Expectations Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution Searching for Privacy Risks in LLM Agents via Simulation SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction Majority Bit-Aware Watermarking For Large Language Models Coward: Collision-based OOD Watermarking for Practical Proactive Federated Backdoor Detection Prompt to Pwn: Automated Exploit Generation for Smart Contracts Activation-Guided Local Editing for Jailbreaking Attacks Random Walk Learning and the Pac-Man Attack ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation White-Basilisk: A Hybrid Model for Code Vulnerability Detection Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model! 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Semantic Non-Assembly: Privacy by Architectural Inertness Under Component Exposure
[Submitted on 21 Jun 2026] · 2026-06-23 · via cs.CR updates on arXiv.org

Computer Science > Cryptography and Security

arXiv:2606.22311 (cs)

[Submitted on 21 Jun 2026]

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Abstract:Existing privacy frameworks emphasize confidentiality, access control, appropriate information flow, or statistical disclosure limitation. We introduce a complementary class of privacy guarantee (Semantic Non-Assembly) in which privacy is characterized not by the difficulty of achieving exposure but by the information yield of exposure when it occurs. SNA prevents evaluation of a designated predicate by preventing any sub-threshold coalition from assembling a sufficient assignment to its input domain. An architecture satisfies Semantic Non-Assembly when no coalition of fewer than a defined threshold of components can assemble such an assignment: complete exposure and decryption of any sub-threshold component yields no actionable data. In the base protocol, the guarantee is structural: it operates through architecture, not policy, and its privacy properties degrade predictably under component compromise rather than collapsing at a single point. The reference instantiation combines this structural guarantee with audited organizational constraints, as characterized in Appendix A. This paper formalizes the guarantee and establishes four ProVerif-verified properties: Device Non-Correlation, Registry Observer Non-Identification, Submission Server Blindness, and Active Defense Gate correctness, the first three through a two-channel provenance architecture. The Birthmark Standard instantiates the guarantee on constrained capture hardware, demonstrating deployability where ZK-based approaches are computationally infeasible. All formal properties and scope limitations are documented in Appendix A.
Comments: 31 pages, 2 figures. Submitted to ACM Transactions on Privacy and Security
Subjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY); Distributed, Parallel, and Cluster Computing (cs.DC)
ACM classes: E.3; C.2.0; K.6.5
Cite as: arXiv:2606.22311 [cs.CR]
  (or arXiv:2606.22311v1 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2606.22311

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Samuel Ryan [view email]
[v1] Sun, 21 Jun 2026 02:40:29 UTC (788 KB)

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