{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TWUB6I3ULFV6IQDEB3VVAR5G3H","short_pith_number":"pith:TWUB6I3U","schema_version":"1.0","canonical_sha256":"9da81f2374596be440640eeb5047a6d9d458e8c433f37e96f9f6c1840add6975","source":{"kind":"arxiv","id":"2506.17621","version":1},"attestation_state":"computed","paper":{"title":"Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Ravishka Rathnasuriya, Wei Yang","submitted_at":"2025-06-21T07:13:14Z","abstract_excerpt":"The growing deployment of deep learning models in real-world environments has intensified the need for efficient inference under strict latency and resource constraints. To meet these demands, dynamic deep learning systems (DDLSs) have emerged, offering input-adaptive computation to optimize runtime efficiency. While these systems succeed in reducing cost, their dynamic nature introduces subtle and underexplored security risks. In particular, input-dependent execution pathways create opportunities for adversaries to degrade efficiency, resulting in excessive latency, energy usage, and potentia"},"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":"2506.17621","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-21T07:13:14Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"4b6781a87909709e0ec281e74cbc3d204022718e18b19c5a2e45ea8b0738058b","abstract_canon_sha256":"8ff0cf7904930c513b3882642a451c16d807d2912a5e03e1166a79fdf4c9056c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:20.571410Z","signature_b64":"AM2ReeI/ATZLtGBVXjxrYPvOY60JNv/VF10l6I72tFK3msJW4bz7ItZz//i9f0pK7w2IVqCNoQXYF+TZEFhMDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9da81f2374596be440640eeb5047a6d9d458e8c433f37e96f9f6c1840add6975","last_reissued_at":"2026-07-05T11:25:20.570936Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:20.570936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Ravishka Rathnasuriya, Wei Yang","submitted_at":"2025-06-21T07:13:14Z","abstract_excerpt":"The growing deployment of deep learning models in real-world environments has intensified the need for efficient inference under strict latency and resource constraints. To meet these demands, dynamic deep learning systems (DDLSs) have emerged, offering input-adaptive computation to optimize runtime efficiency. While these systems succeed in reducing cost, their dynamic nature introduces subtle and underexplored security risks. In particular, input-dependent execution pathways create opportunities for adversaries to degrade efficiency, resulting in excessive latency, energy usage, and potentia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17621","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.17621/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.17621","created_at":"2026-07-05T11:25:20.570989+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.17621v1","created_at":"2026-07-05T11:25:20.570989+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17621","created_at":"2026-07-05T11:25:20.570989+00:00"},{"alias_kind":"pith_short_12","alias_value":"TWUB6I3ULFV6","created_at":"2026-07-05T11:25:20.570989+00:00"},{"alias_kind":"pith_short_16","alias_value":"TWUB6I3ULFV6IQDE","created_at":"2026-07-05T11:25:20.570989+00:00"},{"alias_kind":"pith_short_8","alias_value":"TWUB6I3U","created_at":"2026-07-05T11:25:20.570989+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10987","citing_title":"AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TWUB6I3ULFV6IQDEB3VVAR5G3H","json":"https://pith.science/pith/TWUB6I3ULFV6IQDEB3VVAR5G3H.json","graph_json":"https://pith.science/api/pith-number/TWUB6I3ULFV6IQDEB3VVAR5G3H/graph.json","events_json":"https://pith.science/api/pith-number/TWUB6I3ULFV6IQDEB3VVAR5G3H/events.json","paper":"https://pith.science/paper/TWUB6I3U"},"agent_actions":{"view_html":"https://pith.science/pith/TWUB6I3ULFV6IQDEB3VVAR5G3H","download_json":"https://pith.science/pith/TWUB6I3ULFV6IQDEB3VVAR5G3H.json","view_paper":"https://pith.science/paper/TWUB6I3U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.17621&json=true","fetch_graph":"https://pith.science/api/pith-number/TWUB6I3ULFV6IQDEB3VVAR5G3H/graph.json","fetch_events":"https://pith.science/api/pith-number/TWUB6I3ULFV6IQDEB3VVAR5G3H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TWUB6I3ULFV6IQDEB3VVAR5G3H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TWUB6I3ULFV6IQDEB3VVAR5G3H/action/storage_attestation","attest_author":"https://pith.science/pith/TWUB6I3ULFV6IQDEB3VVAR5G3H/action/author_attestation","sign_citation":"https://pith.science/pith/TWUB6I3ULFV6IQDEB3VVAR5G3H/action/citation_signature","submit_replication":"https://pith.science/pith/TWUB6I3ULFV6IQDEB3VVAR5G3H/action/replication_record"}},"created_at":"2026-07-05T11:25:20.570989+00:00","updated_at":"2026-07-05T11:25:20.570989+00:00"}