{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZHMQ2AJWZTM32FFQJARWYMDTPI","short_pith_number":"pith:ZHMQ2AJW","schema_version":"1.0","canonical_sha256":"c9d90d0136ccd9bd14b048236c30737a09f82c1383a6a8f4807b899bc011b18e","source":{"kind":"arxiv","id":"2505.00817","version":1},"attestation_state":"computed","paper":{"title":"Spill The Beans: Exploiting CPU Cache Side-Channels to Leak Tokens from Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Andrew Adiletta, Berk Sunar","submitted_at":"2025-05-01T19:18:56Z","abstract_excerpt":"Side-channel attacks on shared hardware resources increasingly threaten confidentiality, especially with the rise of Large Language Models (LLMs). In this work, we introduce Spill The Beans, a novel application of cache side-channels to leak tokens generated by an LLM. By co-locating an attack process on the same hardware as the victim model, we flush and reload embedding vectors from the embedding layer, where each token corresponds to a unique embedding vector. When accessed during token generation, it results in a cache hit detectable by our attack on shared lower-level caches.\n  A signific"},"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":"2505.00817","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2025-05-01T19:18:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"081531edc11cf59d4f3ce2367f7050a8a6ab4f2522b8f9ca32aa25c9ef61b358","abstract_canon_sha256":"a5e72d11892b106ed80f5431e7d09840cbc78b7d9ec6fabc7b518bdc27f51021"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:57:39.601649Z","signature_b64":"fQ0+bRcsKBUcjdrECMPMvLCoJC0hvbZubNdF6QGZucxz/8DxB60pkkMsyV95LT1hCZy2ZcGdlkaqjbRuXw7dAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9d90d0136ccd9bd14b048236c30737a09f82c1383a6a8f4807b899bc011b18e","last_reissued_at":"2026-07-05T10:57:39.601131Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:57:39.601131Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spill The Beans: Exploiting CPU Cache Side-Channels to Leak Tokens from Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Andrew Adiletta, Berk Sunar","submitted_at":"2025-05-01T19:18:56Z","abstract_excerpt":"Side-channel attacks on shared hardware resources increasingly threaten confidentiality, especially with the rise of Large Language Models (LLMs). In this work, we introduce Spill The Beans, a novel application of cache side-channels to leak tokens generated by an LLM. By co-locating an attack process on the same hardware as the victim model, we flush and reload embedding vectors from the embedding layer, where each token corresponds to a unique embedding vector. When accessed during token generation, it results in a cache hit detectable by our attack on shared lower-level caches.\n  A signific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00817","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/2505.00817/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":"2505.00817","created_at":"2026-07-05T10:57:39.601189+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.00817v1","created_at":"2026-07-05T10:57:39.601189+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00817","created_at":"2026-07-05T10:57:39.601189+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZHMQ2AJWZTM3","created_at":"2026-07-05T10:57:39.601189+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZHMQ2AJWZTM32FFQ","created_at":"2026-07-05T10:57:39.601189+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZHMQ2AJW","created_at":"2026-07-05T10:57:39.601189+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.04055","citing_title":"Securing Deep Learning Hardware: A Survey of Side-Channel Vulnerabilities and Countermeasures","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZHMQ2AJWZTM32FFQJARWYMDTPI","json":"https://pith.science/pith/ZHMQ2AJWZTM32FFQJARWYMDTPI.json","graph_json":"https://pith.science/api/pith-number/ZHMQ2AJWZTM32FFQJARWYMDTPI/graph.json","events_json":"https://pith.science/api/pith-number/ZHMQ2AJWZTM32FFQJARWYMDTPI/events.json","paper":"https://pith.science/paper/ZHMQ2AJW"},"agent_actions":{"view_html":"https://pith.science/pith/ZHMQ2AJWZTM32FFQJARWYMDTPI","download_json":"https://pith.science/pith/ZHMQ2AJWZTM32FFQJARWYMDTPI.json","view_paper":"https://pith.science/paper/ZHMQ2AJW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.00817&json=true","fetch_graph":"https://pith.science/api/pith-number/ZHMQ2AJWZTM32FFQJARWYMDTPI/graph.json","fetch_events":"https://pith.science/api/pith-number/ZHMQ2AJWZTM32FFQJARWYMDTPI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZHMQ2AJWZTM32FFQJARWYMDTPI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZHMQ2AJWZTM32FFQJARWYMDTPI/action/storage_attestation","attest_author":"https://pith.science/pith/ZHMQ2AJWZTM32FFQJARWYMDTPI/action/author_attestation","sign_citation":"https://pith.science/pith/ZHMQ2AJWZTM32FFQJARWYMDTPI/action/citation_signature","submit_replication":"https://pith.science/pith/ZHMQ2AJWZTM32FFQJARWYMDTPI/action/replication_record"}},"created_at":"2026-07-05T10:57:39.601189+00:00","updated_at":"2026-07-05T10:57:39.601189+00:00"}