{"as_of":"2026-08-10T16:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d6246058281b88700cce42a43cd183bad3361aad9eb456559189dc7fcec74776","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:54:53.613562Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T00:59:20.697040Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.02416","last_updated":"2025-02-12T14:52:56Z","snapshot_observed_at":"2026-08-07T07:19:45.931364Z","submitted_at":"2024-08-05T12:20:39Z","title":"Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models","version":2},"cited_work":{"arxiv_id":"2408.02416","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.02416","snapshot_observed_at":"2026-07-04T00:59:20.697040Z","title":"Why are my prompts leaked? unraveling prompt extraction threats in customized large language models","venue":null,"work_id":"f62c30e5-cb3a-4ddb-9276-4f22a7543008","year":2024},"citing_paper":{"arxiv_id":"2506.02546","last_updated":"2026-04-14T05:32:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-03T07:32:57Z","title":"To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-19T11:30:47.877793Z"},"links":{"cited_paper":"/paper/2408.02416","citing_paper":"/paper/2506.02546"},"observation_digest":"sha256:f13b4ef805ca04204d421bc24055d76be6c256256ae8ab2b1e667da447e5a94d","observation_id":"122f18a0-d412-4dd3-8907-aa78f2d2b866","resolution":{"observed_at":"2026-05-19T11:32:17.381468Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.02416","last_updated":"2025-02-12T14:52:56Z","snapshot_observed_at":"2026-08-07T07:19:45.931364Z","submitted_at":"2024-08-05T12:20:39Z","title":"Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.02416","snapshot_observed_at":"2026-08-07T10:54:53.613562Z","title":"Why are my prompts leaked? unraveling prompt extraction threats in customized large lan- guage models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.04036","last_updated":"2025-06-04T14:58:29Z","snapshot_observed_at":"2026-08-07T22:01:39.666653Z","submitted_at":"2025-06-04T14:58:29Z","title":"Privacy and Security Threat for OpenAI GPTs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T10:54:53.613562Z"},"links":{"cited_paper":"/paper/2408.02416","citing_paper":"/paper/2506.04036"},"observation_digest":"sha256:4bca0c43cfd7fbd7da24e368508782eb3dbe2e747089eb9445bf1de034a579ce","observation_id":"009ef784-4367-4d16-8ada-b42c2898a859","resolution":{"observed_at":"2026-08-07T10:54:53.613562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.02416","last_updated":"2025-02-12T14:52:56Z","snapshot_observed_at":"2026-08-07T07:19:45.931364Z","submitted_at":"2024-08-05T12:20:39Z","title":"Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.02416","snapshot_observed_at":"2026-08-06T22:23:09.752446Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22521","last_updated":"2025-06-26T22:02:01Z","snapshot_observed_at":"2026-08-07T22:00:37.560675Z","submitted_at":"2025-06-26T22:02:01Z","title":"A Survey on Model Extraction Attacks and Defenses for Large Language Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:23:09.752446Z"},"links":{"cited_paper":"/paper/2408.02416","citing_paper":"/paper/2506.22521"},"observation_digest":"sha256:9bfa5b2a7bbb473827fa67a55b436add376bd1dc8b148ae6f8cf635ea9b4ae6c","observation_id":"414498dc-713b-4933-89a7-c561d909e403","resolution":{"observed_at":"2026-08-06T22:23:09.752446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.02416","last_updated":"2025-02-12T14:52:56Z","snapshot_observed_at":"2026-08-07T07:19:45.931364Z","submitted_at":"2024-08-05T12:20:39Z","title":"Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models","version":2},"cited_work":{"arxiv_id":"2408.02416","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.02416","snapshot_observed_at":"2026-07-04T00:59:20.697040Z","title":"Why are my prompts leaked? unraveling prompt extraction threats in customized large language models","venue":null,"work_id":"f62c30e5-cb3a-4ddb-9276-4f22a7543008","year":2024},"citing_paper":{"arxiv_id":"2508.06153","last_updated":"2026-04-16T09:49:06Z","snapshot_observed_at":"2026-07-06T22:09:57.861336Z","submitted_at":"2025-08-08T09:17:33Z","title":"SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-19T00:54:00.464780Z"},"links":{"cited_paper":"/paper/2408.02416","citing_paper":"/paper/2508.06153"},"observation_digest":"sha256:df9f80a8db77d4a553ada35cc8b4b1f4f5d3dfafbc84096fbdeb01dabfcb5f66","observation_id":"8b8826cc-f58b-44a6-a5f9-b5353d3a9a5f","resolution":{"observed_at":"2026-05-19T00:56:56.354399Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.02416","last_updated":"2025-02-12T14:52:56Z","snapshot_observed_at":"2026-08-07T07:19:45.931364Z","submitted_at":"2024-08-05T12:20:39Z","title":"Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models","version":2},"cited_work":{"arxiv_id":"2408.02416","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.02416","snapshot_observed_at":"2026-07-04T00:59:20.697040Z","title":"Why are my prompts leaked? unraveling prompt extraction threats in customized large language models","venue":null,"work_id":"f62c30e5-cb3a-4ddb-9276-4f22a7543008","year":2024},"citing_paper":{"arxiv_id":"2606.18673","last_updated":"2026-06-17T04:20:00Z","snapshot_observed_at":"2026-07-06T23:54:04.739534Z","submitted_at":"2026-06-17T04:20:00Z","title":"Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-26T20:47:36.337189Z"},"links":{"cited_paper":"/paper/2408.02416","citing_paper":"/paper/2606.18673"},"observation_digest":"sha256:ed1c2f5a0622430261365c4d55c1c3afb778fae3151be21378ab30e89bcc8ebd","observation_id":"9a8515bc-a01e-45d7-9006-93550d9b20eb","resolution":{"observed_at":"2026-07-04T00:59:20.699931Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2408.02416/citation-record","integrity":"/paper/2408.02416/integrity","json":"/paper/2408.02416/citation-record.json","paper":"/paper/2408.02416"},"outbound":[],"paper":{"arxiv_id":"2408.02416","last_updated":"2025-02-12T14:52:56Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T07:19:45.931364Z","submitted_at":"2024-08-05T12:20:39Z","title":"Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.02416."}