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We study an information-theoretic trade-off for code LLMs between functional capacity, $\\Cap=\\rmI(c^*;c_\\pi)$, and perturbation retention, $\\Sec=\\rmI(c_\\pi;\\tilde c_\\pi)$. Here $\\Sec$ is a retention-channel quantity, not a direct measure of exploit success or vulnerable-code generation. For code completion modeled as $p\\to c_\\pi$ with perturbed prompt $\\tilde p$, we prove $\\Cap+\\Sec\\le \\rmH(c^*)+\\rmI(p;\\tilde p)$, decomposing the budget in"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.03308","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2026-06-02T08:22:14Z","cross_cats_sorted":[],"title_canon_sha256":"f31586cd2bb1ce6261e2b72fc5f7cc01dc319ccbac0d077d65b5f1b37eed006b","abstract_canon_sha256":"fabb8497558565f41c455e824b7466e37f50cfa3d283c540cb20aac75ac95b7a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:54.598037Z","signature_b64":"pCsqwryWek/SCGWnCGbUAjoG3BJ67wGhQ0Y9H6y2+P5Wy3sofEuEVu69xRqDlf4uE130LUTcTDVP2XpaNPVFBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aaaa3ea92c5c941901fdbdf02a6facd2849eda138359765a66d97124814d1b36","last_reissued_at":"2026-06-03T01:05:54.597705Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:54.597705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Security Budget of Code LLMs: An Information-Theoretic Capacity-Security Bound","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Jianwei Tai","submitted_at":"2026-06-02T08:22:14Z","abstract_excerpt":"AI programming assistants make natural-language prompts a software-development interface, so small prompt perturbations become usability and security risks. 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