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enclawed: A Configurable, Sector-Neutral Hardening Framework for Single-User AI Assistant Gateways

arXiv Security Archived Apr 21, 2026 ✓ Full text saved

arXiv:2604.16838v1 Announce Type: new Abstract: We present enclawed, a hard-fork hardening framework built on top of the OpenClaw single-user personal artificial intelligence (AI) assistant gateway. enclawed targets deployments that need attestable peer trust, deny-by-default external connectivity, signed-module loading, and a tamper-evident audit trail typically regulated industries such as financial services, healthcare, defense contracting, regulated R&D, and government enclaves. The framewor

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    Computer Science > Cryptography and Security [Submitted on 18 Apr 2026] enclawed: A Configurable, Sector-Neutral Hardening Framework for Single-User AI Assistant Gateways Alfredo Metere We present enclawed, a hard-fork hardening framework built on top of the OpenClaw single-user personal artificial intelligence (AI) assistant gateway. enclawed targets deployments that need attestable peer trust, deny-by-default external connectivity, signed-module loading, and a tamper-evident audit trail typically regulated industries such as financial services, healthcare, defense contracting, regulated R&D, and government enclaves. The framework ships in two flavors: an open flavor that preserves OpenClaw compatibility while still emitting audit, classification, and data-loss-prevention (DLP) signals, and an enclaved flavor that activates strict allowlists, Federal Information Processing Standards (FIPS) cryptographic-module assertion, mandatory module-manifest signature verification, and high-assurance peer attestation for the Model Context Protocol (MCP). The classification ladder is fully data-driven: a deploying organization selects from five built-in presets (generic, US-government, healthcare, financial services, three-tier) or supplies its own JSON. We accompany the implementation with a security review, a 204-case test suite (146 unit tests, 58 adversarial pen-tests for tamper detection, signature forgery, egress bypass, trust-root mutation, DLP evasion, prompt injection, and code injection), real-time human-in-the-loop control (per-agent pause / resume / stop and approval queues), a memory-bounded secure transaction buffer with rollback (default cap 50% of system RAM, configurable), a strict-mode TypeScript typecheck of all 22 framework files, and a GitHub Actions workflow ready for continuous integration. enclawed is a hardening framework, not an accredited compliance certification. The deploying organization remains responsible for hardware, validated cryptographic modules, certified facilities, and assessor sign-off. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2604.16838 [cs.CR]   (or arXiv:2604.16838v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.16838 Focus to learn more Submission history From: Alfredo Metere [view email] [v1] Sat, 18 Apr 2026 05:10:11 UTC (31 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 21, 2026
    Archived
    Apr 21, 2026
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