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Governing Dynamic Capabilities: Cryptographic Binding and Reproducibility Verification for AI Agent Tool Use

arXiv Security Archived Mar 17, 2026 ✓ Full text saved

arXiv:2603.14332v1 Announce Type: new Abstract: AI agents dynamically acquire capabilities at runtime via MCP and A2A, yet no framework detects when capabilities change post-authorization. We term this the capability-identity gap}: it enables silent capability escalation and violates EU AI Act traceability requirements. We propose three mechanisms. Capability-bound agent certificates extend X.509 v3 with a skills manifest hash; any tool change invalidates the certificate. Reproducibility commitm

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✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 15 Mar 2026] Governing Dynamic Capabilities: Cryptographic Binding and Reproducibility Verification for AI Agent Tool Use Ziling Zhou AI agents dynamically acquire capabilities at runtime via MCP and A2A, yet no framework detects when capabilities change post-authorization. We term this the capability-identity gap}: it enables silent capability escalation and violates EU AI Act traceability requirements. We propose three mechanisms. Capability-bound agent certificates extend X.509 v3 with a skills manifest hash; any tool change invalidates the certificate. Reproducibility commitments leverage LLM inference near-determinism for post-hoc replay verification. A verifiable interaction ledger provides hash-linked, signed records for multi-agent forensic reconstruction. We formalize nine security properties and prove they hold under a realistic adversary model. Our Rust prototype achieves 97us certificate verification (<1ns capability binding overhead, ~1,200,000 faster than BAID's zkVM), 0.62ms total governance overhead per tool call (0.1--1.2% of typical latency), and 4.7X separation from cross-provider outputs (Cohen's d > 1.0 on all four metrics), with best classification at F_1=0.876 (Jaccard, \theta=0.408); single-provider deployments achieve F_1=0.990 with 11.5 times separation. We evaluate 12 attack scenarios -- silent escalation, tool trojanization, phantom delegation, evidence tampering, collusion, and runtime behavioral attacks validated against NVIDIA's Nemotron-AIQ traces -- each detected with a traceable mechanism, while the MCP+OAuth 2.1 baseline detects none. An end-to-end evaluation over a 5-to-20-agent pipeline with real LLM calls confirms that full governance (G1--G3) adds ~10.8ms per pipeline run (0.12% overhead), scales sub-linearly per agent, and detects all five in-situ attacks with zero false positives. Comments: 17 pages, 5 figures, 19 tables, 46 references Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.14332 [cs.CR]   (or arXiv:2603.14332v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.14332 Focus to learn more Submission history From: Ziling Zhou [view email] [v1] Sun, 15 Mar 2026 11:46:57 UTC (311 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs 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
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    ◬ AI & Machine Learning
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    Mar 17, 2026
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