{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:E3XO7UIZNGEJE2QIRVN233MBKY","short_pith_number":"pith:E3XO7UIZ","schema_version":"1.0","canonical_sha256":"26eeefd1196988926a088d5baded815625c38d03c34eec53cb7b96588a4c9c1f","source":{"kind":"arxiv","id":"2402.03147","version":1},"attestation_state":"computed","paper":{"title":"Detecting Scams Using Large Language Models","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Liming Jiang","submitted_at":"2024-02-05T16:13:54Z","abstract_excerpt":"Large Language Models (LLMs) have gained prominence in various applications, including security. This paper explores the utility of LLMs in scam detection, a critical aspect of cybersecurity. Unlike traditional applications, we propose a novel use case for LLMs to identify scams, such as phishing, advance fee fraud, and romance scams. We present notable security applications of LLMs and discuss the unique challenges posed by scams. Specifically, we outline the key steps involved in building an effective scam detector using LLMs, emphasizing data collection, preprocessing, model selection, trai"},"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":"2402.03147","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CR","submitted_at":"2024-02-05T16:13:54Z","cross_cats_sorted":[],"title_canon_sha256":"ad75eca1d665e5b979f7d8ab3ef2db9fa4e93bc8b8947448e67356d78d889d7b","abstract_canon_sha256":"5e9735e3b3e3b8b7c68310528dc66745e5f9ddb0e4438b69a12009768aac283d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:41:31.742239Z","signature_b64":"+eoWU7ipifehz1pgsHB7QiR8wL0CCX+gsEJaZtNkBKZCrwVGi+HahmNd+IaRwHkD1vFq/DG5jSosWcm0itjXBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26eeefd1196988926a088d5baded815625c38d03c34eec53cb7b96588a4c9c1f","last_reissued_at":"2026-07-05T07:41:31.741796Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:41:31.741796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Scams Using Large Language Models","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Liming Jiang","submitted_at":"2024-02-05T16:13:54Z","abstract_excerpt":"Large Language Models (LLMs) have gained prominence in various applications, including security. This paper explores the utility of LLMs in scam detection, a critical aspect of cybersecurity. Unlike traditional applications, we propose a novel use case for LLMs to identify scams, such as phishing, advance fee fraud, and romance scams. We present notable security applications of LLMs and discuss the unique challenges posed by scams. Specifically, we outline the key steps involved in building an effective scam detector using LLMs, emphasizing data collection, preprocessing, model selection, trai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.03147","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/2402.03147/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":"2402.03147","created_at":"2026-07-05T07:41:31.741853+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.03147v1","created_at":"2026-07-05T07:41:31.741853+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.03147","created_at":"2026-07-05T07:41:31.741853+00:00"},{"alias_kind":"pith_short_12","alias_value":"E3XO7UIZNGEJ","created_at":"2026-07-05T07:41:31.741853+00:00"},{"alias_kind":"pith_short_16","alias_value":"E3XO7UIZNGEJE2QI","created_at":"2026-07-05T07:41:31.741853+00:00"},{"alias_kind":"pith_short_8","alias_value":"E3XO7UIZ","created_at":"2026-07-05T07:41:31.741853+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.16656","citing_title":"Read This Paper to Get $50 Million:* An Analysis of Mobile Messaging Scams Using Reddit Data","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2601.19684","citing_title":"LLM-Assisted Authentication and Fraud Detection","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E3XO7UIZNGEJE2QIRVN233MBKY","json":"https://pith.science/pith/E3XO7UIZNGEJE2QIRVN233MBKY.json","graph_json":"https://pith.science/api/pith-number/E3XO7UIZNGEJE2QIRVN233MBKY/graph.json","events_json":"https://pith.science/api/pith-number/E3XO7UIZNGEJE2QIRVN233MBKY/events.json","paper":"https://pith.science/paper/E3XO7UIZ"},"agent_actions":{"view_html":"https://pith.science/pith/E3XO7UIZNGEJE2QIRVN233MBKY","download_json":"https://pith.science/pith/E3XO7UIZNGEJE2QIRVN233MBKY.json","view_paper":"https://pith.science/paper/E3XO7UIZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.03147&json=true","fetch_graph":"https://pith.science/api/pith-number/E3XO7UIZNGEJE2QIRVN233MBKY/graph.json","fetch_events":"https://pith.science/api/pith-number/E3XO7UIZNGEJE2QIRVN233MBKY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E3XO7UIZNGEJE2QIRVN233MBKY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E3XO7UIZNGEJE2QIRVN233MBKY/action/storage_attestation","attest_author":"https://pith.science/pith/E3XO7UIZNGEJE2QIRVN233MBKY/action/author_attestation","sign_citation":"https://pith.science/pith/E3XO7UIZNGEJE2QIRVN233MBKY/action/citation_signature","submit_replication":"https://pith.science/pith/E3XO7UIZNGEJE2QIRVN233MBKY/action/replication_record"}},"created_at":"2026-07-05T07:41:31.741853+00:00","updated_at":"2026-07-05T07:41:31.741853+00:00"}