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Quantifying Compromise Risk in Exceptional Access Architectures Under Sparse and Indirect Evidence

arXiv Security Archived Jun 18, 2026 ✓ Full text saved

arXiv:2606.19106v1 Announce Type: new Abstract: Lawful exceptional access (EA) systems hold the cryptographic keys that decrypt protected communications for authorised parties. The debate over their risks has been long and qualitative, complicated by two problems: no public dataset of EA-specific compromise events exists, so assessment must use sparse, indirect evidence; and prior work has treated structurally different designs as equivalent, though transmission-layer EA in carrier infrastructur

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    Computer Science > Cryptography and Security [Submitted on 17 Jun 2026] Quantifying Compromise Risk in Exceptional Access Architectures Under Sparse and Indirect Evidence Alan Woodward Lawful exceptional access (EA) systems hold the cryptographic keys that decrypt protected communications for authorised parties. The debate over their risks has been long and qualitative, complicated by two problems: no public dataset of EA-specific compromise events exists, so assessment must use sparse, indirect evidence; and prior work has treated structurally different designs as equivalent, though transmission-layer EA in carrier infrastructure (T-EA) and over-the-top EA at the platform layer (OTT-EA) differ in how cryptographic keys relate to ciphertext data. This paper builds a structured uncertainty framework for evaluating systemic compromise risk in EA architectures. It does not produce predictive forecasts, which the evidence cannot support; it separates findings robust to assumptions from those that depend on calibration. Four analytical layers are applied to T-EA and OTT-EA: three empirical pillars (historical analogues, a Monte Carlo scenario layer, a channel-independence decomposition) plus a Bayesian Structural Risk Model on a parallel-subgraph attack graph. The central findings are structural. First, EA-equipped architectures of either class carry strictly higher modelled risk than their no-EA counterfactual, an ordering independent of calibration. Second, the classes differ in distribution shape: T-EA risk is dominated by central tendency, OTT-EA by the tail under correlated campaigns. Third, calibration-conditional annual probability ranges span 1.4% to 12.9% for T-EA across the structured-judgement targeting-premium interval. Over multi-decade horizons, cumulative compromise is well above zero; key-material exfiltration is irreversible, weighing heavily on OTT-EA's larger user populations. The framework quantifies compromise probability, not expected harm; consequence modelling and benefit estimation are outside its scope. Subjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY) Cite as: arXiv:2606.19106 [cs.CR]   (or arXiv:2606.19106v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.19106 Focus to learn more Submission history From: Alan Woodward [view email] [v1] Wed, 17 Jun 2026 14:18:36 UTC (1,148 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.CY 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
    Published
    Jun 18, 2026
    Archived
    Jun 18, 2026
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