{"as_of":"2026-08-16T15:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:94d767a17962fe37b9a3e2c43134034870dd34c88ad70f8de8bb75330e8c1556","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:21:57.476226Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.01262/citation-record","integrity":"/paper/1908.01262/integrity","json":"/paper/1908.01262/citation-record.json","paper":"/paper/1908.01262"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1901.01142","last_updated":"2019-01-04T14:40:06Z","snapshot_observed_at":"2026-08-14T17:35:07.918066Z","submitted_at":"2019-01-04T14:40:06Z","title":"V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.01142","snapshot_observed_at":"2026-08-14T15:21:57.388160Z","title":"V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.388160Z"},"links":{"cited_paper":"/paper/1901.01142","citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:841e923c2d69a8cd22ba4dd1f323243d0fd1fd73eed971412e43ec8fd5170677","observation_id":"c8aae246-6353-4d71-aee5-cba1ac13a3dc","resolution":{"observed_at":"2026-08-14T15:21:57.388160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.981219Z","title":"Static and dynamic analysis: Synergy and duality","venue":null,"work_id":"bc377bf9-ccf9-43e7-96bc-8442f7a76210","year":2003},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.345360Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:ea98029417c0f54ba0d2dcf4aa8dd0335682bc9e2932d90db26684617048c0b1","observation_id":"ae563068-3605-405e-9c36-8dc2844b80c3","resolution":{"observed_at":"2026-08-14T15:21:57.985668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1402.3722","last_updated":"2014-02-15T21:03:02Z","snapshot_observed_at":"2026-08-14T23:44:32.401718Z","submitted_at":"2014-02-15T21:03:02Z","title":"word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1402.3722","snapshot_observed_at":"2026-08-14T15:21:57.362441Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.362441Z"},"links":{"cited_paper":"/paper/1402.3722","citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:03ee09c163b1bf9b0060729bdeb5c117c5bd305e396ca1fee385c70ec64f60dd","observation_id":"a9ee15f4-38de-401d-8f6d-871925d36c6e","resolution":{"observed_at":"2026-08-14T15:21:57.362441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.929753Z","title":null,"venue":null,"work_id":"6e0ce1f5-8ea6-47c9-92c2-5c83dda8cfe6","year":2013},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.366905Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:aaf5fc37cf97ca5435698ffaf36684045e48b6254815d708f552836111a1b700","observation_id":"3054615f-7424-487b-b5a7-3ebedcadc93c","resolution":{"observed_at":"2026-08-14T15:21:57.933544Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.918021Z","title":"Adaptive Grey-Box Fuzz-Testing with Thompson Sampling","venue":null,"work_id":"793cfe9b-fe10-4a62-9051-ae2e92620835","year":2017},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.370766Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:66c3b7c155c5c9e708e748a1fca54377ade25f4b2ef85e1740925e2a94e3b2dc","observation_id":"4719dc48-1818-40f3-8b50-30dcbf796410","resolution":{"observed_at":"2026-08-14T15:21:57.921998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.858514Z","title":"Pin: building customized program analysis tools with dynamic instrumentation","venue":null,"work_id":"5f4e193a-91c2-47a1-a43a-92725a039eb9","year":2005},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.393933Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:85fec62e80f2ba12fb663e8e5f7f0562874ed048ad541d8076d6efd236637ed6","observation_id":"1b636f98-78ea-421b-bea8-43f735bc69da","resolution":{"observed_at":"2026-08-14T15:21:57.863696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.828347Z","title":"The NIST SARD project [Internet]","venue":null,"work_id":"3cb1169d-cf60-4090-9fc6-fc414e4a1f24","year":2006},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.411731Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:5cb453d12da38b48386891359ecaa25e29f65f8906fae71e3729bcc3c1a3ac00","observation_id":"0203d76e-3d32-40d9-b4d7-74df685dd7df","resolution":{"observed_at":"2026-08-14T15:21:57.832881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.814330Z","title":null,"venue":null,"work_id":"05e5ca8b-44b1-4bf7-a7a0-d101aeb37185","year":2018},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.416663Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:435d0d623f759df473c0382f9ccf655192e6c8c2e7e0f20c43be2cc9f7ed8416","observation_id":"25f7c9a9-b998-462a-ba10-ebeb1cb6bfef","resolution":{"observed_at":"2026-08-14T15:21:57.819382Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.800666Z","title":null,"venue":null,"work_id":"1966c375-1f8e-4268-945f-da8e1037cf33","year":2017},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.420567Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:b6318438b6ae8d60cc1f7934fb626ac1e0cde881f892412ba924932ec7e77809","observation_id":"62aeb3f8-5c68-49c9-a568-e4772964b7fb","resolution":{"observed_at":"2026-08-14T15:21:57.805455Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.785846Z","title":null,"venue":null,"work_id":"40608a91-8c77-4527-9f2e-62c2cf45bfbf","year":2012},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.424754Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:eff32c1bdc6e6f16c568909064371815b3a9f113cf6e01ca8070d78d7fc62780","observation_id":"5d7cf660-35ca-40d3-b86e-0756b8258f9c","resolution":{"observed_at":"2026-08-14T15:21:57.790882Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.768202Z","title":null,"venue":null,"work_id":"cdda7c75-2aa7-4b41-87cc-c3b135bd8c55","year":2019},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.428737Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:500ea1ae8e56722162fe8be00b66b5e11b396ad98875714a255f6b847a78b374","observation_id":"c0a57590-92df-4ebe-8ca0-d292f35e982c","resolution":{"observed_at":"2026-08-14T15:21:57.773056Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.735609Z","title":"Body armor for binaries: preventing buffer overflows without recompilation","venue":null,"work_id":"f5e1f746-9774-4663-8a91-9f72bcf3c4ee","year":2012},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.436217Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:c5df84d5a95fed6ad8b5f94aba26da4bd9b2749eab6536c4dc5bee7c3d988bfe","observation_id":"9e1a9f2f-e80a-4444-8f8c-48d242316ccc","resolution":{"observed_at":"2026-08-14T15:21:57.742372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.705580Z","title":"Exniffer: Learning to Prioritize Crashes by Assessing the Exploitability from Memory Dump","venue":null,"work_id":"2acf5f3f-c73a-4e70-bb7b-712982414b86","year":2017},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.446571Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:40419cad95e8764bc52824dfbc0cc1621f0186d93ac877a7e4fefb3bdd24f0a8","observation_id":"82b37032-81da-4cd6-b1e1-02bd06bc21c1","resolution":{"observed_at":"2026-08-14T15:21:57.710234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.689503Z","title":null,"venue":null,"work_id":"44865e52-5e99-44c3-a656-2fa2a3e248cd","year":2019},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.451688Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:13cf2f013e2cf9528ebecc04c7cc5577089a2b53a84f032d6a56663201b2280f","observation_id":"3fb0696d-181a-4424-8dc5-8a3b544df612","resolution":{"observed_at":"2026-08-14T15:21:57.694129Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.646177Z","title":"Vulnerability detection with deep learning","venue":null,"work_id":"549efbab-94a6-4fcd-a26c-3c353dc112b0","year":2017},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.460188Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:55393340f76a5bcc369e6fe8bdcfd8f7fb8938266a3967cfc1b8831745f77de7","observation_id":"5f7747bd-df19-45c8-bf7a-c8419838d34b","resolution":{"observed_at":"2026-08-14T15:21:57.653759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.627518Z","title":"1298–302","venue":null,"work_id":"08025906-3a75-49cb-a879-3d7adc32b685","year":2009},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.464498Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:ed4aa79920c38c0c5922c88f425cd8209b57f4e1cddbad4408975c48b397986e","observation_id":"266822d8-0646-45a3-b1b8-6f473c660342","resolution":{"observed_at":"2026-08-14T15:21:57.631720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.753143Z","title":"Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware","venue":null,"work_id":"c0edf579-a9b0-40b2-ba0b-bf1bbc055e21","year":2015},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.432414Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:d0d0ab86a18994f5ec75045c1cb74e917c3e931b8a381197c3d02a569aa5e349","observation_id":"2942ebbc-3d08-4f40-9cc4-2e3ad8fb8ad5","resolution":{"observed_at":"2026-08-14T15:21:57.757551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.614394Z","title":null,"venue":null,"work_id":"f56d1609-afd9-401a-8674-57eb0067e9e9","year":2017},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.468546Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:946138a38e7e108ad2d6fc275f6b59d1e524cad7b394a64d19c253705260f8ed","observation_id":"cf972b61-5a5d-4bc8-aa0c-c366b5523c9a","resolution":{"observed_at":"2026-08-14T15:21:57.618612Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.598610Z","title":"Available from: http://lcamtuf.coredump.cx/afl/ Zhang G, Zhou X, Luo Y, Wu X, Min E","venue":null,"work_id":"8f0759d4-38bd-472e-8880-03170213abaf","year":2019},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.472480Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:28c9fb6c56e64d9ee248c9dfa26604022db7009cfb04043644db8e89f96e9a01","observation_id":"38cbc460-5634-432c-ae89-9a19c431ac9d","resolution":{"observed_at":"2026-08-14T15:21:57.603228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.583540Z","title":"From automation to intelligence: Survey of research on vulnerability discovery techniques","venue":null,"work_id":"7ba60999-77b9-4b2f-a3ee-eab662c59b7e","year":2018},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.476226Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:0656de937a30dfd4435edda053af9915c39e07d3cc59633f9f17721dc27d315c","observation_id":"3a1abbb6-136f-4627-9923-ce5dd41362c9","resolution":{"observed_at":"2026-08-14T15:21:57.588138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.666680Z","title":"Fitness function [Internet]; 2019a [cited 2019 Jul 17]","venue":null,"work_id":"b4ce5f73-6129-4ec8-87d0-aa27cbc27662","year":2019},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.455896Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:c59e48e0d92b1b603799874774d868275f3eb20fda13a7c6cef1c1c1ce635145","observation_id":"0eba6fee-088f-47d1-a2fe-3047fe0eaeac","resolution":{"observed_at":"2026-08-14T15:21:57.674220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.02606","last_updated":"2019-06-03T15:20:47Z","snapshot_observed_at":"2026-08-14T18:54:45.745778Z","submitted_at":"2018-07-07T03:19:25Z","title":"SmartSeed: Smart Seed Generation for Efficient Fuzzing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.02606","snapshot_observed_at":"2026-08-14T15:21:57.397837Z","title":"SmartSeed: Smart Seed Generation for Efficient Fuzzing; arXiv preprint arXiv:1807.02606","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":190,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.397837Z"},"links":{"cited_paper":"/paper/1807.02606","citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:674ca67b680bfd12929e041e4bb749cc8cc1d9e774e4f4453518591849357340","observation_id":"be191059-c22a-4d0b-866a-4d17c5b3fbdb","resolution":{"observed_at":"2026-08-14T15:21:57.397837Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.875231Z","title":"Efficient backprop","venue":null,"work_id":"4775048c-ecd5-4146-8a89-ac48258a6fb4","year":2012},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":436,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.383379Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:bda444b4209a86f72ab9038bb0e0da33994b537c7ef84535cda2c3a6902bd221","observation_id":"8a961462-0b3c-48cb-a866-8910d5d34e78","resolution":{"observed_at":"2026-08-14T15:21:57.879622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.994760Z","title":null,"venue":null,"work_id":"bd82a6ce-6817-4001-8636-f591938b3181","year":2014},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":1992,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.332923Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:a6d9c102de559de6390a90cc11b0dd2a191d5a769ab5a4aa862fab8f9c27f40c","observation_id":"2cf55fe4-8071-4212-ab8a-b05291d4f511","resolution":{"observed_at":"2026-08-14T15:21:57.998911Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.719432Z","title":"!exploitable [Internet]","venue":null,"work_id":"b973d682-835d-455c-9d4f-7cf33d25e7a4","year":2013},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":1998,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.440173Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:ca877b9eaf1b63469277a7780468e655ff97cb01089497c2bb55148bc445ad80","observation_id":"55eb3714-6b8e-4f16-b75f-1e994d25cff7","resolution":{"observed_at":"2026-08-14T15:21:57.723438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.841541Z","title":null,"venue":null,"work_id":"a408a5de-2d5a-44c5-8d12-90e58e837e85","year":1990},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2001,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.402548Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:6954eefe0474dec73cc7c47b095bc74b6e1b0df2ad817e0ba80a101acbb0c360","observation_id":"0131c391-0a65-4bb3-8225-263fb6ebc6fe","resolution":{"observed_at":"2026-08-14T15:21:57.849101Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.969343Z","title":null,"venue":null,"work_id":"3e10395f-6f6c-4ce9-a6e8-f2d1ac54c4a8","year":2000},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2003,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.350356Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:67ae4e69d7c211bc50694b74c4a393c9e6c2a434138b14af150e6dee981568f0","observation_id":"9732c6b4-dcad-4837-9abe-18bd0fe4601b","resolution":{"observed_at":"2026-08-14T15:21:57.973258Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:58.046361Z","title":"Unleashing Mayhem on Binary Code","venue":null,"work_id":"de2857d3-e54f-4304-aba9-60c9a99c06bb","year":2012},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2006,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.306897Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:1c379933c0e940dfdef7fcfeb91b2f10fbcfaaee8df8123ebfb6febda59de397","observation_id":"78f5d573-29b2-4ad3-a8c5-f1bc2dc9c1e2","resolution":{"observed_at":"2026-08-14T15:21:58.050587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.941815Z","title":"Big Code","venue":null,"work_id":"6eae8e9e-472c-4ae8-92ec-46d77eaa8774","year":2019},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2007,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.358404Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:d7acbc6b50f5c7758da929ad75fb8269debade247d5a110298a286a25256a5ab","observation_id":"e1520a98-2d1a-4e87-ba56-984bfb3dd9ed","resolution":{"observed_at":"2026-08-14T15:21:57.946326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:58.007664Z","title":null,"venue":null,"work_id":"f21630f5-bce5-455d-be89-9d040956c720","year":2018},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.328702Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:f432ead34e968852fd08c8755af70b96b7cbd91cc9d68fd0404f69824a27f353","observation_id":"4a4e6289-3629-4780-9082-65a5795a26ac","resolution":{"observed_at":"2026-08-14T15:21:58.012709Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.905281Z","title":null,"venue":null,"work_id":"8f6e109c-23d1-4534-9cc0-b95619b1f65c","year":1976},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.374540Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:b72642f7a9b343cbd277ef4199bc1a983de1b7f5895093d6b510abf06b7464c5","observation_id":"2dbf1c17-cf7d-4e19-aee1-4cba018fad98","resolution":{"observed_at":"2026-08-14T15:21:57.909765Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:58.071804Z","title":null,"venue":null,"work_id":"ec591f45-0f53-432f-ab96-ebbb80dba9d5","year":2018},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.295950Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:e5e9bcd8aa2be9c227611184a8cd48fc785870e2b43aad5eae971d8b8be9b642","observation_id":"1a099f6c-1f68-4583-9828-18c1927f651d","resolution":{"observed_at":"2026-08-14T15:21:58.076554Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:58.059689Z","title":null,"venue":null,"work_id":"64dd7a07-ca9a-4524-911b-90e8f5cb8e7f","year":2008},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.301107Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:6dd9b69119457b20366c8839f07aa68e9271ad1a984b6b2a1237feb97d0e653a","observation_id":"f4f6d31d-fce8-4e8c-8ed2-e7f7d0aa967b","resolution":{"observed_at":"2026-08-14T15:21:58.064132Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:58.034020Z","title":null,"venue":null,"work_id":"1ddc93bc-ddbc-49dd-a442-0e123cd3e4f1","year":2018},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.313611Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:825cd7004272092d8fad4f880f055ed4d3445f70c34a6524b3557def9d5a6c51","observation_id":"014d810c-54e2-423e-8f04-78b9bdc419a5","resolution":{"observed_at":"2026-08-14T15:21:58.037971Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.889642Z","title":"Available from: https://lcamtuf.blogspot.com/2014/08/binary-fuzzing-strategies-whatworks.html LeCun Y, Bengio Y, Hinton G","venue":null,"work_id":"89cbc31b-7754-4a29-87df-5606e33aab54","year":2019},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.379393Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:6dc89d6c25e73e73cb4b52521d49be0da22348497e06071f51aed68962354c49","observation_id":"cbdf84d3-2769-4de7-ac3d-2313baf29e77","resolution":{"observed_at":"2026-08-14T15:21:57.895975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.02807","last_updated":"2017-11-08T02:43:18Z","snapshot_observed_at":"2026-08-14T20:15:50.936655Z","submitted_at":"2017-11-08T02:43:18Z","title":"Faster Fuzzing: Reinitialization with Deep Neural Models","version":1},"cited_work":{"arxiv_id":"1711.02807","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.02807","snapshot_observed_at":"2026-08-14T15:21:57.510073Z","title":"Faster Fuzzing: Reinitialization with Deep Neural Models","venue":"cs.AI","work_id":"aa94c0b9-533d-4f12-85a6-5634fa03ff5e","year":2017},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.406450Z"},"links":{"cited_paper":"/paper/1711.02807","citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:1349488dda3277c6993de8b47226660becadcb7c0d5a695eabba040dfed75d89","observation_id":"f87bdb39-7cb4-4a56-a8f6-34163b61bc7e","resolution":{"observed_at":"2026-08-14T15:21:57.517137Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.07490","last_updated":"2018-07-19T15:22:35Z","snapshot_observed_at":"2026-08-14T18:49:56.574567Z","submitted_at":"2018-07-19T15:22:35Z","title":"FuzzerGym: A Competitive Framework for Fuzzing and Learning","version":1},"cited_work":{"arxiv_id":"1807.07490","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.07490","snapshot_observed_at":"2026-08-14T15:21:57.567429Z","title":"FuzzerGym: A Competitive Framework for Fuzzing and Learning","venue":"cs.SE","work_id":"f33eb911-a530-46b3-8511-8285725a253b","year":2018},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.337844Z"},"links":{"cited_paper":"/paper/1807.07490","citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:a6ebfdd8ec420dbb3c41654b8c913eec991dd2d44bbf22d8b8f10fa081007a17","observation_id":"e4139b1a-7b0c-4cc3-9b7f-9cfca9f7684e","resolution":{"observed_at":"2026-08-14T15:21:57.572769Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:57.957440Z","title":null,"venue":null,"work_id":"3bb95e80-f24b-44a6-aab3-3826a441761a","year":2017},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.354574Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:b20fd0e67b14a51d0dc0aa9d47f0baebd0ed35b2b412f63e94f3b07f64d38a86","observation_id":"ac6a9300-64fe-4574-9bd8-42ce473b63ea","resolution":{"observed_at":"2026-08-14T15:21:57.961457Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:58.021168Z","title":null,"venue":null,"work_id":"7a50a55b-17ca-4590-9eb2-914ee9134dc0","year":2013},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.323655Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:031f3db3abc78a36edd5af35fb43bb79a1189e8a74e8d3ff0e23eae3082f7f4f","observation_id":"5e214198-5680-4726-b8a2-8701e56a7460","resolution":{"observed_at":"2026-08-14T15:21:58.025368Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:21:58.085285Z","title":"AEG: Automatic exploit generation","venue":null,"work_id":"70a39df3-be13-43c6-9533-9e33941c3579","year":2014},"citing_paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-14T15:21:57.291155Z"},"links":{"citing_paper":"/paper/1908.01262"},"observation_digest":"sha256:8fe18917e985ec7881e1cf20436c14a80ffbc983b5d17e0336f67d6c164287ec","observation_id":"d222394c-f324-4cae-9fd5-61dc4fca8d1c","resolution":{"observed_at":"2026-08-14T15:21:58.089231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.01262","last_updated":"2019-08-04T02:51:53Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-16T06:26:20.314814Z","submitted_at":"2019-08-04T02:51:53Z","title":"A systematic review of fuzzing based on machine learning techniques"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":2,"verified_fuzzy":18},"total_outbound_references":40},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:1908.01262."}