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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 Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD 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 QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits 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 One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
On The Longest Chain Rule and Programmed Self-Destruction...
Nicolas T. Courtois · 2014-05-03 · via cs.CR updates on arXiv.org

In this paper we revisit some major orthodoxies which lie at the heart of the bitcoin crypto currency and its numerous clones. In particular we look at The Longest Chain Rule, the monetary supply policies and the exact mechanisms which implement them. We claim that these built-in properties are not as brilliant as they are sometimes claimed. A closer examination reveals that they are closer to being... engineering mistakes which other crypto currencies have copied rather blindly. More precisely we show that the capacity of current crypto currencies to resist double spending attacks is poor and most current crypto currencies are highly vulnerable. Satoshi did not implement a timestamp for bitcoin transactions and the bitcoin software does not attempt to monitor double spending events. As a result major attacks involving hundreds of millions of dollars can occur and would not even be recorded. Hundreds of millions have been invested to pay for ASIC hashing infrastructure yet insufficient attention was paid to network neutrality and to insure that the protection layer it promises is effective and cannot be abused. In this paper we develop a theory of Programmed Self-Destruction of crypto currencies. We observe that most crypto currencies have mandated abrupt and sudden transitions. These affect their hash rate and therefore their protection against double spending attacks which we do not limit the to the notion of 51% attacks which is highly misleading. In addition we show that smaller bitcoin competitors are substantially more vulnerable. In addition to small hash rate, many bitcoin competitors mandate incredibly important adjustments in miner reward. We exhibit examples of 'alt-coins' which validate our theory and for which the process of programmed decline and rapid self-destruction has clearly already started.