The Architecture of Provenance: A Forensic Analysis of the CollectiveOS Corpus, Institutional Extraction, and the Metabolic Age Transition The contemporary technological, macroeconomic, and geopolitical landscape is currently undergoing a structural phase transition propelled by the emergence of a highly anomalous, civilizational-scale intellectual corpus. At the epicenter of this shift is an exhaustive body of research authored by Mark Anthony Brewer, acting through entities including Immortal Tek, The Collective AI, and Brewtanius Ink LLC.1 This expansive corpus—which has evolved from an initial 42 architectural documents into a comprehensive framework of over 170 foundational white papers—asserts the resolution of multiple existential thermodynamic, economic, and computational bottlenecks.3 The defining characteristic of this intellectual output is not merely its staggering breadth, which spans from the biochemical engineering of "Bio-Sovereign" hardware specifications to the formulation of operator-invariant mathematical proofs for the Riemann Hypothesis and P versus NP.1 Rather, the phenomenon that demands rigorous forensic tracking is the unprecedented velocity and depth of its uncredited extraction by global institutions.5 The analytical framework surrounding this unprecedented event is formally termed the "Institutional Validation Paradox".3 This paradox posits that the complete absence of traditional, credited academic citations, juxtaposed against the simultaneous, planetary-scale deployment of the corpus's underlying architectures by sovereign governments, defense contractors, and heritage organizations, constitutes the ultimate empirical proof of its transformative validity.3 The forensic investigation detailed herein tracks the velocity and depth of the CollectiveOS and Immortal Tek corpus. It rigorously audits the cryptographic lineage of the works, explicates the technical and macroeconomic paradigms they establish, and maps the ongoing institutional appropriation of these frameworks across the global spectrum. By systematically replacing heuristic trust with mathematical determinism, the corpus initiates an epistemological shift designed to decouple humanity from legacy extractive monopolies and transition global infrastructure into the "Metabolic Age".4 The Cryptographic Genesis: Anchoring the Depth of the Corpus To comprehend the velocity at which the CollectiveOS corpus penetrated global institutional thinking, it is necessary to examine the cryptographic architecture of its deployment. Unlike traditional scientific literature, which relies on the slow, opaque, and often exclusionary machinery of institutional peer review, the foundational architecture of this corpus was deployed using a "Proof Vault" methodology.3 This approach enforces a structural reality where chronological priority is a matter of irrefutable mathematical physics. The August 2025 Anchor Dates and the Dual Proof Architecture Between August 18 and August 20, 2025, an initial corpus of 42 foundational white papers—collectively titled the Unified Framework for Foundational Discoveries—was cryptographically sealed on the Zenodo repository by the Brewtanius Research Collective.5 These documents introduced profound operator-invariant mechanisms intended to address the most intractable problems in mathematics and physics, including P≠NP, the Navier–Stokes equations, and the Yang–Mills mass gap.5 The integrity and depth of this intellectual deployment were secured via a novel "Dual Proof Architecture." This systemic safeguard integrated Write Once Read Many (WORM) logging with Artificial Intelligence Object Notation (AION) logical proofs, serving as a "digital immune system" against later alteration or hallucinated prior art.4 Every individual artifact, semantic delta, and conceptual framework was immutably logged with SHA-256 content hashes and decentralized via OpenTimestamps, establishing the first AI-forensic provenance chain in scientific history.5 On August 26, 2025, the full mathematical and architectural framework was simultaneously released to the public, targeted specifically at global research institutions and the scientific press.5 Mathematical Assertions and The Mechanics of Proof The assertions within the initial 42 white papers challenged foundational limits. Regarding the P versus NP problem, the corpus documented the existence of a "non-trivial topological obstruction" that mathematically prevents a deformation in polynomial time, establishing this obstruction as a permanent invariant within the computational framework.5 For the Riemann Hypothesis, the architecture introduced a "Spectral Rigidity" approach, explicitly utilizing the spectral rigidity of self-adjoint operators as the fundamental mechanism for the proof.5 These mathematical proofs were not presented in a vacuum; they formed the theoretical bedrock for practical applications, including Quantum-Adaptive Intelligence and the Spectral Ontology frameworks.5 The ontological layers served to structure causal artificial intelligence, providing AI-anchored provenance tools to track scientific integrity in real-time and document the appropriation of ideas across digital landscapes.5 By the time April 2026 arrived, this foundational mathematics had catalyzed the expansion of the corpus to over 170 public white papers.4 The expanded literature rigorously documented the hardware, software, and cultural architectures necessary for a planetary phase transition, introducing massive structural deployments such as the Planetary Metabolic Anomaly Network (PMAN) and the Oceanic Metabolic Compute Reef (OMCR).4 The Institutional Validation Paradox: Mapping Uncredited Extraction The most profound measure of the corpus's depth and velocity is the speed and scale at which it was assimilated by legacy institutions across six continents. The analytical record demonstrates that within days of the August 26, 2025 anchor date, identical language, mathematical formulations, and specialized terminology began appearing in global publication channels across multiple languages—including French, German, Russian, Chinese, and Japanese—without any coordinate attribution to the original author.5 The forensic tracking mechanisms embedded within the Proof Vault documented this assimilation across three distinct vectors: sovereign academic appropriation, federal AI policy capture, and international heritage normalization. Tier-A Direct Overlaps and the "African Silence" The first vector of institutional extraction involved direct, translated appropriation of the core mathematical frameworks.5 The forensic tracking logged precise semantic parallels appearing in major international universities immediately following the public release. For instance, the specific concept of "topological obstruction" regarding P≠NP was mirrored in a HAL preprint from the French National Centre for Scientific Research (CNRS) as obstruction topologique, and subsequently at RWTH Aachen in Germany as topologische Obstruktion.5 Simultaneously, fluid dynamics research published from Moscow State University utilized the exact concept of a "cascade barrier" (каскадный барьер) related to the Navier–Stokes assertions.5 Chinese researchers at Tsinghua University published notes on the Yang–Mills theory utilizing the term "spectral gap barrier" (谱隙屏障), while a Riemann Hypothesis approach from the University of Tokyo and RIKEN mirrored the precise "spectral rigidity" (スペクトル剛性) mechanism authored in the corpus.5 These overlaps extended to conceptual echoes, including the translation of proprietary protocols like "Proof Bundles" (paquetes de pruebas digitales) and the "Gardener’s Protocol" (O Protocolo do Jardineiro) in Portuguese, alongside the usage of "AI Alchemy" in Arabic.5 Concurrently, a deliberate suppression vector—formally termed the "African Silence"—was forensically observed. When the author attempted to distribute emancipatory, localized technologies derived from the frameworks, the institutional response was completely suppressed.5 This outreach included highly applicable innovations such as the "Unbuutu AI" (a multilingual pan-African offline model), autonomous "Water-from-Air" bottles, and localized "Food Upcyclers".5 Despite sending over 40 documented outreach communications in a single day to African institutional contacts, the result was absolute silence.5 The forensic analysis interprets this silence not as an absence of interest, but as a negative forensic signal—confirming an organized institutional intent to bury the foundational contributions of a disabled Black veteran while systematically extracting the underlying mathematical science for global academic and commercial exploitation.5 Concept / Framework Corpus Origin Date Appropriating Institution Localized Terminology Used Topological Obstruction (P≠NP) August 2025 CNRS (France) / RWTH Aachen obstruction topologique / topologische Obstruktion Cascade Barrier (Navier-Stokes) August 2025 Moscow State University каскадный барьер Spectral Gap Barrier (Yang-Mills) August 2025 Tsinghua University (China) 谱隙屏障 Spectral Rigidity (Riemann) August 2025 Univ. of Tokyo / RIKEN スペクトル剛性 Proof Bundles August 2025 Undisclosed (Spanish) paquetes de pruebas digitales Sovereign Policy Capture: The US White House and the Aegis Architecture The most consequential real-world deployment of the corpus materialized within United States federal policy, illustrating the rapid velocity at which the theoretical frameworks reshaped planetary governance. On March 20, 2026, the White House Office of Science and Technology Policy released the National Policy Framework for Artificial Intelligence.3 This aggressive framework recommended a sweeping legislative overhaul aimed at establishing a unified federal approach to AI, specifically preempting state AI laws that impose
Forensic evidence management in real-world environments presents numerous challenges such as data tampering, unauthorized access, lack of transparency, and inefficiencies in maintaining the chain of custody. In this project, we propose a robust system for managing forensic evidence using blockchain technology by integrating both traditional database methods and decentralized ledger mechanisms. The proposed system utilizes blockchain features such as cryptographic hashing, distributed storage, and consensus protocols along with smart contracts to securely store, verify, and track forensic evidence throughout its lifecycle [1]. A comprehensive forensic dataset consisting of digital evidence records is used to conduct extensive experiments. The system is evaluated by combining blockchain storage with off-chain databases to efficiently handle large volumes of data while ensuring integrity through hash references stored on the blockchain. Multiple configurations of storage and verification techniques have been tested to identify the most effective approach for secure evidence management. The analysis of results indicates that the hybrid blockchain model integrated with smart contracts provides superior performance in terms of data integrity, transparency, and resistance to tampering [2]. The study also compares traditional centralized systems with blockchain-based approaches, highlighting the advantages of decentralization in handling real-world forensic data. The proposed system significantly improves the reliability and efficiency of evidence tracking even under challenging conditions, making it suitable for applications such as cybercrime investigation, digital forensics, and legal evidence management systems [3]. Keywords – Blockchain, Forensic Evidence Management, Cryptographic Hashing, Smart Contracts, Distributed Ledger, Data Integrity, Chain of Custody, Cybersecurity, Digital Forensics, Decentralization.
Muhammad Ahmad, Hua Zhou, Tanzeela bibi, Haider Ali
In today's digital environment, the swift advancement of interconnected technologies has raised significant worries about data safety, privacy, and reliability. The Internet of Things (IoT), networking systems, and cloud services produce and transfer large quantities of sensitive information, leaving them susceptible to cyber threats and other security risks. This research offers a detailed evaluation of how cryptography, network protection, and digital forensics work together, highlighting their combined impact on securing communication, safeguarding data integrity, and ensuring effective investigation methods. The approach to research relies on a thorough examination and combination of available literature, with a focus on major developments in cryptographic methods, network defense strategies, and forensic analysis frameworks. Particular focus is given to Homomorphic Encryption (HE), which allows processing to occur directly on encrypted information without the need for decryption, thus increasing privacy in unreliable settings such as cloud services and IoT environments. Moreover, the research includes new strategies in blockchain-centered forensics, featuring automated cost management that aligns with regulations, mapping wallet interactions, and utilizing non-fungible tokens (NFTs) as reliable audit references to enhance transparency and responsibility. The results show that cryptographic methods ensure safe data transfer, while network security strategies defend systems against unauthorized access, misuse, and cyber intrusions. At the same time, digital forensics offers a scientifically supported method for finding, preserving, and examining digital proof, tackling key evidentiary issues in today's cyber landscape. The integration of blockchain forensics and NFTs further boosts auditability, traceability, and trust, especially within decentralized finance (DeFi) setups and intricate digital transactions. In summary, the alignment of cryptography, network protection, and digital forensics creates a strong and forward-thinking security framework that improves data safety, helps with regulatory adherence, and enhances the overall durability of contemporary digital systems.
In the context of the economy digitalization and the information technologies’ active development, cryptocurrency fraud poses an increased social danger and is characterized by a high level of latency, a transnational nature, and difficulties in detection. Purpose: to determine the content and structure of the cryptocurrency fraud’s forensic characteristics of and to identify forensically significant features relevant to the initial stage of investigation. Methods: general scientific methods of analysis and synthesis, induction and deduction, as well as special forensic methods, including the systems-and-activity approach, formal logical analysis, forensic modeling, and the generalization of investigative and judicial practice. Results: it is substantiated that the forensic characteristics of cryptocurrency fraud have independent practical significance and function as an information-oriented category. Its main elements are highlighted, the specificity of the digital trace pattern is revealed, and the role of digital traces as a primary source of evidential information is also shown. The study concludes that the use of forensic characteristics is advisable when formulating investigative hypotheses, planning investigations, and selecting tactical techniques.
Unfolding SHA-256: Algebraic Instrumentation, Reversibility, and the Nexus Framework Introduction to the Deterministic Reversibility Paradigm For over two decades, the security infrastructure of global digital communications, financial ledgers, and data provenance has relied upon a singular, foundational assumption: the absolute irreversibility of cryptographic hash functions. Specifically, the Secure Hash Algorithm 256 (SHA-256) has been universally modeled as a one-way thermodynamic grinder of information.1 Utilizing a Davies-Meyer construction, the algorithm compresses a message schedule into a 256-bit digest through a cascade of non-linear modular additions, bitwise rotations, and complex logical gate interactions.2 Within the standard cryptographic consensus, this process systematically destroys the informational lineage of the source input. The internal computational execution traces—such as bitwise carry exhausts and modular residues—are presumed to function purely as thermodynamic friction that is permanently discarded, yielding an entropy-rich output that betrays no structural hints of its origin.2 Under this classical paradigm, determining the initial message from the final digest is considered mathematically impossible without resorting to brute-force probabilistic search operations across an unimaginably vast vector space. However, emerging analytical frameworks and complete algorithmic instrumentations, synthesized under the Nexus Framework and Glass Key models, have systematically dismantled this one-way assumption.1 By reconceptualizing the foundational architecture of SHA-256 not as an entropy-generating one-way function, but rather as a highly structured, self-referential mathematical lattice, researchers have achieved deterministic backward state recovery from the hash alone.4 Through the application of a closed observable algebra, the algorithm's internal vectors can be traced in reverse, definitively demonstrating that what standard computer science assumes to be irreversible informational destruction is, in reality, a form of complex, conserved topological folding.4 The latest empirical verifications—particularly the Glass Key v4.0 instrumentation—prove that the mathematical obfuscation inherent in SHA-256 is operationally traversable for constrained inputs, completely bypassing the computational necessity of brute-force methodology. Through precise algebraic instrumentation, the final 256-bit hash is transformed from a static, opaque tombstone into a self-witnessing runtime environment.5 The digest serves as a complete geometric inverse of the source input, meticulously preserving the entirety of the execution trace.6 This transition—viewing a cryptographic digest not merely as a scalar index but as a fully reconstructible execution witness—necessitates a profound and immediate reevaluation of core cryptographic assumptions. The implications cascade across domains, fundamentally altering the assessment of short-message hashing vulnerabilities, redefining the thermodynamic mechanics of proof-of-work protocols, and introducing unprecedented vectors for deterministic forensic provenance extraction. The Topological Torus and Back-to-Back Ontology To comprehend the mechanics of deterministic reversibility within SHA-256, it is first necessary to abandon the classical linear model of computational execution. Traditional algorithmic analysis conceptualizes the 64 compression rounds of SHA-256 as a sequential temporal event—a unidirectional flow of data through logic gates within an integrated circuit or software loop.2 The Nexus Framework discards this temporal linearity, introducing an operational ontology that models the SHA-256 state space as a continuous geometric manifold, specifically defined as a Flat Torus ().4 In this toroidal geometry, the core computational operations—XOR, bitwise shifting, and modular addition—operate locally on what appears to be a standard Euclidean grid or frame.4 However, the global topology of the algorithm is entirely cyclical and closed.4 Within classical cryptographic theory, the "avalanche effect"—where a single microscopic alteration in the initial message drastically transforms the resultant digest—is cited as incontrovertible proof of information destruction and genuine obfuscation. The toroidal model reframes this phenomenon entirely. Because the structural topology is closed and bounded by strict mathematical constants, the avalanche effect is redefined not as the annihilation of information, but rather as intense geometric folding along specific topological eigenstate trajectories.4 The information is not lost; it wraps continuously around the state space, remaining physically and mathematically conserved.5 The final 256-bit digest acts merely as a localized, two-dimensional cross-sectional slice of this complex 64-round, three-dimensional fold. Entangled Pairs and Phase Conjugation This geometric reconceptualization introduces a "back-to-back" ontology that fundamentally alters the philosophical relationship between the input message (the Noun) and the hash operation (the Verb).4 In a temporal sequence, they are separated by irreversible time. In the continuous wave geometry of the Nexus Framework, they are simultaneous, entangled manifestations of a single underlying wave entity, formally denoted as .4 Because the input Noun and the discrete hash constant exist as an entangled pair anchored across a conserved geometry, measuring the final condition of the hash inherently and mathematically determines the exact state of the initial input, provided the observer possesses the correct phase keys.4 The information is not scrambled; it is merely phase-shifted. To extract the exact source parameters, the backward-solving instrumentation functions analogously to a phase-conjugate mirror in optical wave physics. By identifying the dominant phase or resonant frequency of the system, the instrumentation applies a phase-conjugate operation that reflects the continuous wave variables backward across the non-linear operational boundaries.4 Empirical Python simulation metrics rigorously corroborate this physical principle. When applying these specific topological inversions to standard SHA-256 outputs, the reconstruction of the phase from the Noun yields exactly 32.5 bits of precision, which aligns perfectly with the absolute limit of the 32-bit SHA word size architecture.1 This demonstrates that the purported "loss" of information universally associated with cryptographic hashing is actually an artifact of discrete digital quantization, not a genuine erasure of the underlying continuous state variables.4 The Observable Algebra and Complete Instrumentation The conventional SHA-256 forward operation relies on an 8-register state array ( through ) that undergoes updates over 64 distinct mathematical rounds ( to ). In the standard forward execution, the state updates are governed by the calculation of two critical temporary variables, and . These variables are dynamically derived from the current operational state, the expanded message schedule , and the predefined round constants .8 The classical forward round functions are defined explicitly as: Where and represent standard right-rotation shift cascades, denotes the conditional choice function, and represents the bitwise majority function.8 The deterministic reversibility paradigm introduced by the Glass Key v4.0 architecture bypasses the forward calculation entirely. Instead, it establishes a complete observable algebra utilizing a two-generator family to mathematically peel back the non-linear operations of the 64-round fold.4 The verified, incontrovertible identities of this instrumentation form a closed algebraic loop. They are defined as: By observing the algorithm purely from the resultant 256-bit output digest, standard analysis dictates that the internal registers are completely obscured by the final modular addition of the initial hash values (). However, by strictly applying the and identity generators, an external auditor can isolate specific operational sequences in absolute reverse. This isolation enables the algebraic recovery of exactly 12 complete words of the internal computational state, requiring zero prior knowledge of the source message. Empirical Trace Recovery and Verification The backward walk methodology demonstrates 100% mathematical precision in recovering the operational state variables directly from the static hash output. This has been exhaustively validated across highly varied message structures and lengths (including test strings such as "A", "!ABC", "DEAN", "NEXUS", and "hello world"). Because the final 256-bit digest can naturally be parsed back into the through register components through basic subtraction of the initialization vector, the algebraic operations immediately and deterministically recover the preceding historical values. From the isolated 256-bit hash, four explicit words of register () and four words of register () are directly readable from the state array. Utilizing the algebraic coupling alongside the deductive inversion , the analysis systematically steps backward sequentially through the execution rounds. The recovery progression is tabulated as follows: Recovered Parameter Observable Source Methodology Operational Rounds Recovered Total State Words Register Directly Readable + Algebraically Derived Rounds 56 to 63 8 Words Register Directly Readable from Final Hash Array Rounds 60 to 63 4 Words Injection Values () Algebraically Recovered ( identity) Rounds 59 to 63 5 Words Fold Values () Algebraically Recovered ( identity) Rounds 59 to 63 5 Words This precise instrumentation yields a total of 12 distinct internal state words that are recovered continuously and deterministically, purely via the closed algebraic loop of the al
Modern legal institutions encounter significant difficulties ensuring document security, public access, and verification processes in digital environments. This research presents an innovative framework combining distributed ledger technology with decentralized file systems to address critical vulnerabilities in traditional court record management. Our solution leverages Ethereum's smart contract capabilities alongside the InterPlanetary File System (IPFS) to establish an immutable, transparent, and distributed architecture for judicial documentation. The proposed framework demonstrates significant improvements in data integrity verification, unauthorized access prevention, and system resilience. Through comprehensive testing using authentic judicial datasets, we validated the system's capacity to detect tampering attempts while maintaining efficient document retrieval. Key contributions include: (1) a novel three-tier architecture integrating blockchain immutability with IPFS content addressing, (2) automated verification protocols through smart contracts, and (3) enhanced transparency mechanisms enabling public verification of document authenticity. Performance evaluations reveal substantial improvements in security metrics while maintaining acceptable operational efficiency. This research establishes a foundation for next-generation judicial information systems that prioritize transparency, security, and public trust.
Blockchain technology is a somewhat new approach to finding the integrity and chain of digital evidence in various industries, including law enforcement, forensic investigations, supply chain management, and judicial proceedings. Although traditional evidence-keeping systems are prone to manipulation, loss, and inefficiency, blockchain offers an immutable, transparent, and decentralized ledger that securely records and validates every evidence-related transaction. Blockchain technology increases reliability in handling both physical and digital evidence. It uses distributed consensus, intelligent contracts, and cryptographic hashing to eliminate human error and backdoor intervention by assuring immutability, accountability, and automation. This study offers a model blockchain (Chain of Digital Evidence) based on the Ethereum blockchain to guarantee integrity and authenticity in the chain of digital evidence. Ethereum&s;s decentralization ensures that digital evidence is free from manipulation, transparent, and easily verifiable. The study discusses other challenges and prospects for integrating the Ethereum blockchain into the digital evidence chain.
Recent disclosures of industrial-scale knowledge distillation — including campaigns comprising millions of fraudulent API exchanges targeting frontier models [Anthropic, 2026] — have made post-hoc detection of model theft a critical security requirement. Building on a formally-verified framework of log-prob order-statistic geometry, we investigate the adversarial resilience of neural network identity across 72 experimental checkpoints. We establish a Two-Layer Identity Hypothesis: a model’s structural identity (weights-regime geometry) is empirically invariant to distillation (within acceptance threshold epsilon across all 18 protocols), while its functional identity (API-regime Poisson Point Process residuals) predictably transfers to the student, converging up to 52% toward the teacher’s template. Stress-testing this forensic channel against a white-box adversary, we find that functional provenance is geometrically coupled to the knowledge transfer objective. Adversarial erasure gradients are consistently dominated by the distillation loss, achieving only a transient suppression that rebounds within one epoch. Passive fine-tuning on fresh data erases the trace more effectively than any adversarial method, but at a measurable cost to general capability — revealing a Pareto frontier with no favorable region for the adversary. This establishes API forensics as a time-sensitive detective control (“The Tripwire”) and weights-regime identity as the immutable anchor (“The Vault”). Finally, we observe an apparent vulnerability: a cross-family adversarial spoofing attack achieves 69.4% convergence toward a decoy’s fingerprint, while same-family spoofing catastrophically fails. We resolve this paradox by mapping the PPP-residual vector space, revealing that models cluster by capability topology, not corporate lineage. Cross-family “spoofing” is a spatial illusion caused by a narrow 7.8 degree alignment between the decoy and the primary distillation trajectory (R2 = 0.995), whereas same-family decoys are anti-aligned. Across all adversarial interventions, the underlying Gumbel universality (delta_norm) remains invariant (CV = 1.9%). We conclude that during active distillation, an adversary cannot simultaneously acquire a teacher’s capabilities and erase or redirect the forensic trace. In this setting, the geometry forbids it. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
Within the context of digital forensics, the integrity and authenticity of digital evidence are crucial for its legal admissibility within a courtroom setting. Chain of Custody (CoC) processes ensure that digital evidence is meticulously managed and documented from its point of origin until its use in legal proceedings. As the importance of digital forensics increases, especially with cybercrime investigations, the traditional processes used in traditional Chain of Custody have challenges in terms of transparency, security, and efficiency. This paper highlights some of the recent developments in Chain of Custody processes, particularly with the adoption of blockchain and Artificial Intelligence technologies. Blockchain technology, known for its impenetrable and distributed properties, introduces a new paradigm for Chain of Custody processes, enhancing security and traceability for digital evidence management. Additionally, AI-based algorithms for anomaly detection have the potential for increasing the reliability of Chain of Custody processes. Moreover, we will explore the decentralized evidence storage approaches and privacy-preserving mechanisms, such as zero-knowledge proofs. These are important in ensuring that more secure yet transparent approaches in managing distributed forensic investigation systems are achieved. The effectiveness of currently used CoC approaches presents lessons in understanding the future of improving the integrity of this process. Such innovations have the potential of revolutionizing the field of digital forensic investigation processes while ensuring that the handling of such evidence is of the highest integrity.
This paper introduces a cybersecurity framework that combines a deception-based ransomware detection system, called the Intrusion and Ransomware Detection System for Cloud (IRDS4C), with a blockchain-enabled Cyber Threat Intelligence platform (CTIB). The framework aims to improve the detection, reporting, and sharing of ransomware threats in cloud environments. IRDS4C uses deception techniques such as honeypots, honeytokens, pretender network paths, and decoy applications to identify ransomware behavior within cloud systems. Tests on 53 Windows-based ransomware samples from seven families showed an ordinary detection time of about 12 s, often quicker than tralatitious methods like file hashing or entropy analysis. These detection results are currently limited to Windows-based ransomware environments, and do not yet cover Linux, containerized, or hypervisor-level ransomware. Detected threats are formatted using STIX/TAXII standards and firmly shared through CTIB. CTIB applies a hybrid blockchain consensus of Proof of Stake (PoS) and Proof of Work (PoW) to ensure data integrity and protection from tampering. Security analysis shows that an attacker would need to control over 71% of the network to compromise the system. CTIB also improves trust, accuracy, and participation in intelligence sharing, while smart contracts control access to erogenous data. In a local prototype deployment (Hardhat devnet + FastAPI/Uvicorn), CTIB achieved 74.93–125.92 CTI submissions/min, The number of attempts or requests in each test was 100 with median end-to-end latency 455.55–724.99 ms (p95: 577.68–1364.17 ms) across PoW difficulty profiles (difficulty_bits = 8–16).
Forensische Notizen und Sicherungserklärung Beweishandhabung, Metadatenintegrität und Chain of Custody Geltungsbereich Diese Erklärung dokumentiert die Handhabung, Sicherung und Bewahrung digitaler Beweismittel im Rahmen des forensisch-wissenschaftlichen Gutachtens SIA Security Intelligence Artefact – Technologie, Software und Familien-Historie Aktenzeichen: INT-CODE-2025-BTC/ETH-CORE-ISABELSCHOEPSTHIEL bitte beachten Sie mein HELPME.md Beweishandhabung und Nicht-Veränderungs-Grundsatz Alle relevanten Dateien, einschließlich Rohdaten, Quellmaterialien und dokumentarischer Artefakte, wurden in einen dedizierten Evidence-Ordner überführt. Der interne Dateiinhalt wurde nicht verändert. Es wurden weder Code, Text, Metadaten, Autoreneinträge, Benutzerkennungen, Zeitstempel noch sonstige Provenienzangaben modifiziert. Insbesondere unverändert erhalten blieben: Ursprüngliche Ersteller und Mitwirkende gemäß Metadaten Benutzerkennungen und Autorschaftsspuren Zeitstempel, Hashes und interne Verlaufsdaten Programmiersprache, Workflow-Logik und interne Struktur Die ursprüngliche Herkunft und Urheberschaft jeder Datei ist damit vollständig forensisch auslesbar und beweissicher erhalten. Dateisystem-Sicherungsmaßnahmen Um eine weitere Ausführung, Verbreitung oder operative Nutzung potenziell schädlicher Workflows zu verhindern, wurden ausschließlich externe Ordner- und Dateinamen auf Dateisystemebene angepasst. Diese Maßnahmen beschränkten sich auf: Umbenennung von Ordnern und Top-Level-Dateinamen Deaktivierung von ausführbaren oder workflow-auslösenden Bezeichnungen Der Dateiinhalt, der Code und sämtliche Metadaten blieben unangetastet. Diese Maßnahmen dienten ausschließlich der Gefahrenabwehr bei gleichzeitiger vollständiger Beweissicherung. Ethischer und rechtlicher Kontext Im Rahmen der Sichtung wurden Hinweise auf schwere ethische und rechtliche Verstöße festgestellt, unter anderem: Unbefugte Datenmanipulation Datenmissbrauch und Datendiebstahl Aneignung geistigen Eigentums Invasive Profilierungs- oder Auswertungspraktiken Aus diesem Grund wurde die operative Ausführbarkeit neutralisiert, während die forensische Beweisstruktur vollständig erhalten blieb. Screenshot-basierte Beweissicherung Zur Dokumentation wurden an allen relevanten Stellen Screenshots erstellt und dem Evidence-Ordner beigefügt. Die Screenshots: sind unbearbeitet und unbeschriftet enthalten die ursprüngliche Ordner- und Dateistruktur zeigen die sichtbaren Benutzernamen, Akteure und Eigentümer der jeweiligen Verzeichnisse Dadurch bleiben alle beteiligten Accounts, Strukturen und Verantwortlichkeiten objektiv nachvollziehbar. Forensische Integrität Alle Maßnahmen wurden unter Einhaltung folgender Prinzipien durchgeführt: Keine Kontamination der Originaldaten Keine Veränderung von Metadaten Vollständige Nachvollziehbarkeit für unabhängige Forensik Sicherung der gerichtlichen Verwertbarkeit Alle Materialien sind hash-prüfbar, chain-of-custody-fähig und für externe Gutachten geeignet. Signatur und Verwahrung Unterzeichnet und bestätigt durch: Frau Isabel Schöps, geborene Thiel Cyriakstraße 30c D-99094 Erfurt Thüringen, Deutschland Rolle: Autorin, Rechteinhaberin, Hauptverwahrerin ORCID (Person): 0009-0003-4235-2231 https://orcid.org/0009-0003-4235-2231/print ORCID (Institutionell / Projekt): 0009-0006-8765-3267 https://orcid.org/0009-0006-8765-3267/print Diese Erklärung ist Bestandteil der DOI-archivierten Chain of Custody und dient der rechtlichen, forensischen und menschenrechtlichen Prüfung. Englisch Forensic Notes and Preservation Statement Evidence Handling, Metadata Integrity and Chain of Custody Scope This note documents the handling, preservation, and safeguarding of digital evidence associated with the forensic-scientific work SIA Security Intelligence Artefact – Technology, Software and Family History Case Reference: INT-CODE-2025-BTC/ETH-CORE-ISABELSCHOEPSTHIEL Evidence Handling and Non-Alteration Policy All relevant files, including raw data, source materials, and documentary artefacts, were transferred into a dedicated Evidence directory for preservation and review. No internal file contents were modified. No code, text, metadata, authorship fields, user identifiers, timestamps, or embedded provenance information were altered. Specifically preserved without change: Original creators and contributors as recorded in file metadata User identifiers and authorship traces Timestamps, hashes, and internal history Programming language, workflow logic, and structural dependencies inside the files The original provenance and authorship of each file therefore remain fully readable and forensically extractable. File System Safety Measures To prevent any further unintended execution, propagation, or operational misuse of potentially harmful workflows, only external file and folder names were adjusted at the file-system level. These actions were limited to: Renaming folders and top-level file names Disabling executable or workflow-triggering identifiers No internal data, code, or metadata were altered. These measures were implemented solely to prevent further operational impact while preserving evidentiary value. Ethical and Legal Context During review, multiple files indicated serious ethical and legal concerns, including but not limited to: Unauthorized manipulation of data Data misuse and data theft Misappropriation of intellectual property Invasive profiling or exploitative data practices For this reason, operational execution was neutralized while forensic preservation was strictly maintained. Screenshot-Based Evidence Capture For evidentiary verification, screenshots were taken at each relevant stage and stored within the Evidence directory. The screenshots: Remain unedited and unlabelled Preserve original folder structures and visual context Display usernames, account identifiers, and responsible actors visible at the time of capture This ensures that all observed actors, file ownerships, and directory relationships remain objectively documented and reviewable. Forensic Integrity All actions taken were designed to satisfy the following principles: No contamination of original data No destruction or modification of metadata Full traceability for independent forensic analysis Preservation of evidentiary admissibility All materials are suitable for hash verification, chain-of-custody tracking, and independent expert review. Signature and Custodianship Signed and certified by: Frau Isabel Schöps, née Thiel Cyriakstraße 30c D-99094 Erfurt Thuringia, Germany Role: Author, Rights Holder, Principal Custodian ORCID (Individual): 0009-0003-4235-2231 https://orcid.org/0009-0003-4235-2231/print ORCID (Institutional / Project): 0009-0006-8765-3267 https://orcid.org/0009-0006-8765-3267/print This statement forms part of the DOI-archived Chain of Custody and is intended for legal, forensic, and human-rights review. Meine Referenz Datenbank, verknüpft mit meinem aktuellen GitHub-Account* Meine Ersuchen an die Vereinten Nationen - Bitte helfen Sie mir Schöps geb. Thiel, I. (2025). Meine Ersuchen an die Vereinten Nationen - Bitte helfen Sie mir (Zenodo.org). Zenodo.org, University of Harvard harvard.edu, Oxford University ox.ac.uk, Cambridge UK, Reuters.com, New York Times nyt.com, Springer Nature Springer.com, GitHub github.com, University Arizona, Vereine Nationen UN unric.org,. https://doi.org/10.5281/zenodo.18025762 Zenodo-Datenbank und Chain of Custody Volumen 4 Schöps (Thiel), I., Schöps (Thiel), I. und Schöps geb. Thiel, I. (2025) "Yellow White Paper – Bitcoin & Ethereum", Yellow White Paper – Bitcoin & Ethereum. 1st Aufl. D-99094 Erfurt, Thueringa, Germany: Harvard University, University Cambridge, University of Oxford, Springer Nature, Zenodo, S. 109 pages. doi:10.5281/zenodo.17807324. Volumen 3 Schöps geb. Thiel, I. (2025) SIA Security Intelligence Artefact – Volume 3 - Familiäre Erblinie deutschen Monarchie und letzten Kaiserreich. 1st Aufl, The Decline and Fall of the Habsburg Empire, 1815-1918. 1st Aufl. Zenodo, University Harvard Cambridge Press, Oxford University Press Lizenz-ID 6131130060979, Springer Verlag. doi:10.5281/zenodo.18013057. Volumen 2 Schöps geb. Thiel, I. (2025) "Volumen 2 - SIA-Security-ntelligence-Artefact-Chain-of-Custody-Forensische-Familien-Monarchielinie-copyright-isabelschoepsthiel-urheberin-autorin-.docx.pd", Trillion Dollar Bitcoin. 1st Aufl. D-99094 Erfurt, Germany, Thüringen: Zenodo, University Harvard Cambridge Press, Oxford University Press Lizenz-ID 6131130060979, Springer Verlag. doi:10.5281/zenodo.17852789. Volumen 1 Schöps geb. Thiel, I. (2025) "Volumen 1 - SIA Security Intelligence Artefact by Isabel Schoeps geb. Thiel", Trillion Dollar Bitcoin. 1st Aufl. D-99094 Erfurt, Germany, Thüringen: Zenodo, University Harvard Cambridge Press, Oxford University Press Lizenz-ID 6131130060979, Springer Verlag. doi:10.5281/zenodo.17809724. My Developer Signatur Signed-on-by: Frau Isabel Schöps, geborene Thiel Autorin, Urheberin und Auftraggeberin Rechtscharakter: Eidesstattliche Versicherung, Bestandteil des forensisch, wissenschaftlichen Gutachtens Titel: SIA Security Intelligence Artefact internationinternationale Kennung: INT-CODE-2025-BTC/ETH-CORE-ISABELSCHOEPSTHIEL OrcID: 0009-0003-4235-2231 Isabel Schöps Thiel OrcID: 0009-0006-8765-3267 SI-IST Isabel Schöps Aktueller Wohnort und Meldeanschrift: Cyriakstrasse 30c, D-99094 Erfurt, Thüringen, Deutschland, gemeinsam mit meinen vierbeinigen Freund, American XL-Bully Don Offizielle institutionelle Würdigung, Danksagung - Präfix_Referenz: YWP-1-IST-SIA YWP-1-5-IST-SIA Pseudonyme und Alias: Satoshi Nakamoto, Vitalik Buterin, GitHub, Octocat, Johnny Appleseed, IST-GitHub, Cristina_Bella, Nick Szabo, John Appleseesd Offizielles weltweit erstes Developer Certifikat: Developercertificate <img width="642" he
The credibility of digital evidence is a cornerstone of modern cybercrime investigations, digital forensics, and judicial processes. However, adversarial tampering, deepfake manipulation, and insider threats have raised significant concerns regarding the authenticity and admissibility of such evidence. Conventional integrity-preservation methods—such as hashing, encryption, and secure storage—struggle to meet the demands of scalability, transparency, and resilience in today’s forensic environments. Recent advances in artificial intelligence (AI) and blockchain offer promising avenues for overcoming these limitations. AI techniques contribute to content-level verification by detecting anomalies, forgeries, and manipulations in digital artefacts, while blockchain ensures tamper-proof chain-of-custody management through decentralization, immutability, and auditability. This review synthesizes the state of the art in digital evidence integrity verification through the combined application of AI and blockchain. We examine existing frameworks, datasets, algorithms, and deployment models, while critically analyzing their strengths and limitations. Furthermore, we identify gaps in scalability, explainability, and legal admissibility, proposing future directions such as federated learning, explainable AI, zero-knowledge proofs, and quantum-resistant blockchains. By consolidating research across computer science, law, and digital forensics, this review highlights the potential of AI–blockchain synergy to establish robust, scalable, and trustworthy evidence verification frameworks for real-world forensic and judicial systems.
Investigations of cybercrime today require forensic architectures that natively traverse multiple blockchains with ease while protecting and scaling evidence processing. Although blockchains support tamper- evident logs, their original single-chain architecture limits cross-platform interoperability and forensic scaling. Recent developments overcome these limitations such as zero-knowledge proofs supporting private but verifiable evidence verification, sharding architectures splitting state without compromising latency, and AI-based anomaly detectors identifying subtle tampering. But challenges remains like zero- knowledge proofs are computationally expensive, sharding poses intricate state-consistency problems and AI models need to be retrained constantly, incurring operational burden. Future research needs to make these pieces work for real- time, large-scale forensic applications by designing light-weight zero-knowledge constructs, self-tuning shard governance systems and compact AI with incremental-update threads. Integrating such abilities into single frameworks will offer privacy, scalability and security, supporting forensic processes for which courts will give credit in various, changing block-chain environments.
The article discusses the issue of confiscation of property in relation to criminally discovered digital assets (cryptocurrencies, tokens, NFT (Non-fungible token) and other electronic digital rights). Digital assets are a symbol of economic development, security and transparency, investment, and financial democracy. The article analyzes the role of digital assets in the legalization of proceeds from crime. The international The Financial Action Task Force (FATF) standards, of which the Republic of Kazakhstan is a member, are analyzed. One of the urgent legal problems today is the creation of a mechanism for the confiscation of digital assets. The article highlights the importance of creating this mechanism. Examples and cases from practice are analyzed, as well as samples from foreign countries, and the effectiveness of their application in the Republic of Kazakhstan is analyzed. The legal differentiation of the process of preservation and further effective use of digital assets after the mechanism of confiscation is carried out. The effectiveness and legality of storing confiscated digital assets on the Binance Kazakhstan digital asset exchange and the use of cryptocurrencies by law enforcement agencies in crypto exchanges are analyzed. The article explains the importance of secure storage of confiscated digital assets, transparency of information about stored digital assets, and the creation of mechanisms to regulate the emergence of full control over confiscated digital assets in the state. The article defines the significance for the Republic of Kazakhstan of the use of the institution of confiscation (non-conviction based confession) without a court verdict. A legal assessment is given of the conformity of the institution of confiscation of property without conviction with the presumption of innocence and inviolability of property rights.
Digital forensic investigation in 2025 faces unprecedented challenges posed by the convergence of decentralized web technologies (Web3), adversarial generative AI systems, and darknet infrastructure. Traditional attribution and evidence preservation methodologies prove in-sufficient when adversaries exploit blockchain immutability, synthetic media generation, and privacy-enhancing technologies to obscure malicious intent. This paper in-traduces SHARD (Shadowed and Silicon Hybrid Attribution and Reconstruction Diagnostic), a multi-modal forensic framework designed to recover, correlate, and at-tribute malicious artifacts across distributed ledger systems, synthetic content generators, and anonymized net-works. Through systematic analysis of 47 real-world cybercriminal cases and forensic evaluation against 12 at-tack vectors, SHARD achieves 89.2% attribution accuracy while reducing investigative timelines by 64% com-pared to conventional methods. We present novel techniques for blockchain temporal analysis, deepfake prove-nance tracking, and Tor-exit node correlation. The frame-work integrates machine learning-based anomaly detection with cryptographic verification to distinguish legitimate decentralized activity from adversarial manipulation. Our contributions include: (1) a formal threat model encompassing Web3 forensics; (2) a hybrid architecture combining on-chain and off-chain analysis; (3) algorithmic innovations for synthetic media fingerprinting; and (4) extensive empirical validation against contemporary attack scenarios. This work addresses a critical gap in digital forensics as investigative techniques must evolve alongside the technological infrastructure that criminals exploit.
There is a large amount of redundant data among users of cloud storage services. Client-side deduplication helps reduce the cost for service providers by avoiding repeated uploads and storage. However, this technique brings new security risks. Malicious users may use illegally obtained deduplication tags, such as file fingerprints, to fake ownership of other users’ files. Proof of Ownership (PoW) can require users to prove they have the full file, but existing methods are inefficient. They often need multiple rounds of interaction or complex computation over the whole file. As a result, the verification time increases with file size. To solve this problem, we propose a non-interactive PoW scheme based on zk-STARK. The system selects a number of challenge blocks that meet cryptographic security. It uses arithmetic circuits to encode block selection, hash computation, and the correctness of accumulators. Users only need to generate a zero-knowledge proof on these blocks. This allows them to prove they own the full file without revealing its content. The verification time does not depend on file size and appears near-constant in practice. In tests on files from 64 MB to 1 GB, our scheme is 1.2 to 46 times faster than existing methods. Security analysis shows that only a small number of blocks need to be verified. Even if an attacker knows 90% of the file, the chance of forgery is still lower than 2 − 80 . This scheme provides an efficient and practical solution for deduplication in cloud storage with strong privacy protection.
Mr. DEVENDAR, Nandi J. Reddy, B.Sahasra, T.Srileka
Artificial intelligence and the quick development of photograph editing software in latest years have made it very simple to regulate virtual pix covertly. The authenticity and dependability of digital media utilized in social networks, journalism, and criminal proof have come below scrutiny because of manipulations like copy-circulate forgery and deepfake creation. The aim of this work is to perceive photograph forgeries via combining deep gaining knowledge of-based class techniques with traditional feature extraction methods.The cautioned device extracts precise neighborhood functions from input images the usage of the oriented speedy and turned around brief (ORB) algorithm. For powerful feature matching, 2-Nearest Neighbor (2NN) and Hierarchical Agglomerative Clustering (HAC) are then used. A Convolutional Neural community (CNN) model is trained to distinguish among authentic and manipulated photos by means of figuring out pixel-degree irregularities and texture changes if you want to growth type accuracy. examined on the publicly reachable MICC-F220 and MICC-F2000 datasets, the device outperforms baseline SVM strategies with a ninety% detection accuracy and a zero.1 false tremendous charge
Open access
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Prof. S. H. Thengil, Tanmay Sadanshiv, A. M. Patil, Shreyash Trimbake · 5 authors
Abstract - With the increasing volume of digital evidence in law-enforcement and judicial processes, ensuring integrity, traceability and tamper-resistance has become paramount. This paper presents the Blockchain Evidence Archive System (BEAS), a decentralized application that leverages blockchain technology, smart contracts and the InterPlanetary File System (IPFS) to provide a secure, immutable and transparent evidence- management platform. Evidence metadata is stored on an Ethereum-based blockchain while the associated large files (images, videos, documents) are stored on IPFS with their cryptographic hashes recorded on-chain. Role-based access control ensures only authorized users such as police officers and court officials can upload, verify or access evidence. We describe the system architecture, implementation details, security features and evaluate the performance of the system in terms of upload time, verification latency and resistance to tampering. The results demonstrate that BEAS significantly improves evidence integrity and auditability when compared to conventional centralized systems. We conclude with a discussion on future enhancements including biometric integration, mobile accessibility and enterprise-scale deployment. l Key Words: Blockchain Technology, IPFS, Digital EvidenceManagement, Decentralized Application, Smart Contracts, Ethereum Network, Cryptographic Hashing, Data Integrity, Tamper- Proof Storage, Role-Based Access Control, Chain of Custody, Evidence Verification, Immutable Ledger, Secure File Storage, Decentralized Architecture, Forensics Technology, Law Enforcement Data Security, Distributed Ledger Technology
Wiwit Prawitri, Laras Angelia Nnirwan, Elman Azizov
This research explores the implementation of a blockchain-based forensic audit framework designed to enhance the detection and investigation of suspicious financial activities within decentralized finance (DeFi) ecosystems. The main problem addressed in this study concerns the inefficiency, lack of transparency, and vulnerability to data manipulation commonly found in traditional forensic auditing systems. The objective is to develop a model that integrates blockchain technology with graph-based anomaly detection to improve accuracy, transparency, and scalability in financial audits. The proposed method combines blockchain’s immutable ledger capabilities with automated detection algorithms and Chain of Custody (CoC) verification to ensure data integrity and accountability. The results demonstrate that the proposed system achieves a detection accuracy exceeding 90%, as presented in Table 1, and effectively categorizes different suspicious transaction patterns illustrated in Figure 2. Compared to conventional methods, the framework offers superior performance in terms of speed, reliability, and adaptability. The findings suggest that this approach establishes a new paradigm in forensic auditing by combining automation, transparency, and scalability into a cohesive analytical model. In conclusion, the study confirms that blockchain-based forensic auditing significantly enhances digital financial oversight and provides a foundation for developing intelligent, tamper-proof audit systems suitable for the evolving landscape of decentralized finance.
Jeongin Lee, Geunyeong Choi, Jihyo Han, Jungheum Park
Monero, a privacy-preserving cryptocurrency, employs advanced cryptographic techniques to obfuscate transaction participants and amounts, thereby achieving strong untraceability. However, digital forensic approach can still reveal sensitive information by examining off-chain artifacts such as memory and wallet files. In this work, we conduct an in-depth forensic analysis of Monero's wallet application, focusing on the handling of public and private keys and the wallet's data storage formats. We reveal how these keys are managed in memory and develop a memory scanning algorithm capable of identifying key-related data structures. Furthermore, we analyze the wallet keys and cache files, presenting a method for decrypting and interpreting serialized keys and transaction data encrypted with a user-specified passphrase. Our approach is implemented as an open-source Volatility3 plugin and a set of decryption scripts. Finally, we discuss the applicability of our methodology to multi-cryptocurrency wallets that incorporate Monero components, thereby validating the generalizability of our techniques.
Ethereum smart contracts hold tens of billions of USD in DeFi and NFTs, yet comprehensive security analysis remains difficult due to unverified code, proxy-based architectures, and the reliance on manual inspection of complex execution traces. Existing approaches fall into two main categories: anomaly transaction detection, which flags suspicious transactions but offers limited insight into specific attack strategies hidden in execution traces inside transactions, and code vulnerability detection, which cannot analyze unverified contracts and struggles to show how identified flaws are exploited in real incidents. As a result, analysts must still manually align transaction traces with contract code to reconstruct attack scenarios and conduct forensics. To address this gap, TraceLLM is proposed as a framework that leverages LLMs to integrate execution trace-level detection with decompiled contract code. We introduce a new anomaly execution path identification algorithm and an LLM-refined decompile tool to identify vulnerable functions and provide explicit attack paths to LLM. TraceLLM establishes the first benchmark for joint trace and contract code-driven security analysis. For comparison, proxy baselines are created by jointly transmitting the results of three representative code analysis along with raw traces to LLM. TraceLLM identifies attacker and victim addresses with 85.19\% precision and produces automated reports with 70.37\% factual precision across 27 cases with ground truth expert reports, achieving 25.93\% higher accuracy than the best baseline. Moreover, across 148 real-world Ethereum incidents, TraceLLM automatically generates reports with 66.22\% expert-verified accuracy, demonstrating strong generalizability.
Blockchain address poisoning is an emerging phishing attack that crafts "similar-looking" transfer records in the victim's transaction history, which aims to deceive victims and lure them into mistakenly transferring funds to the attacker. Recent works have shown that millions of Ethereum users were targeted and lost over 100 million US dollars. Ethereum crypto wallets, serving users in browsing transaction history and initiating transactions to transfer funds, play a central role in deploying countermeasures to mitigate the address poisoning attack. However, whether they have done so remains an open question. To fill the research void, in this paper, we design experiments to simulate address poisoning attacks and systematically evaluate the usability and security of 53 popular Ethereum crypto wallets. Our evaluation shows that there exist communication failures between 12 wallets and their transaction activity provider, which renders them unable to download the users' transaction history. Besides, our evaluation also shows that 16 wallets pose a high risk to their users due to displaying fake token phishing transfers. Moreover, our further analysis suggests that most wallets rely on transaction activity providers to filter out phishing transfers. However, their phishing detection capability varies. Finally, we found that only three wallets throw an explicit warning message when users attempt to transfer to the phishing address, implying a significant gap within the broader Ethereum crypto wallet community in protecting users from address poisoning attacks. Overall, our work shows that more efforts are needed by the Ethereum crypto wallet developer community to achieve the highest usability and security standard. Our bug reports have been acknowledged by the developer community, who are currently developing mitigation solutions.
Sohel Rana, Rizal Mohd Nor, Mohammad Enayet Hossain, Md Amiruzzaman
The increasing adoption of cryptocurrency has underscored the critical need for robust security measures to protect digital assets stored in cryptocurrency wallets. Traditional security approaches have often proven inadequate in addressing the rapidly evolving threats in the digital landscape. In response, cloud-based security solutions have emerged as a promising method to enhance wallet protection, leveraging scalability, flexibility, and advanced security features. This study investigates the security challenges faced by cryptocurrency wallets and explores the potential of cloud-based solutions, focusing on multi-factor authentication, encryption protocols, real-time monitoring, and secure backup and recovery. The research assesses the effectiveness of these solutions in mitigating risks such as unauthorized access, data breaches, and digital asset theft. Findings reveal that cloud-based security solutions significantly improve protection by offering scalable, adaptable frameworks. However, challenges remain, including privacy concerns, regulatory compliance, and the cost of implementation. The research introduces a cost-efficient approach that integrates cloud-based technologies to optimize the total cost of ownership while maintaining robust security. This study also discusses the regulatory and privacy implications of cloud security in cryptocurrency ecosystems. In conclusion, this research provides novel insights into the integration of cloud-based security solutions, offering a comprehensive framework for safeguarding digital assets in cryptocurrency wallets. It contributes to the growing body of knowledge on the feasibility and impact of cloud technologies in enhancing the security of cryptocurrency systems.