{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:4ZEUFWBCNN6UL4JUDNMX3GNQPM","short_pith_number":"pith:4ZEUFWBC","canonical_record":{"source":{"id":"1803.01307","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-03-04T06:56:42Z","cross_cats_sorted":[],"title_canon_sha256":"7a2fc869111d22717ee8b8509790f26a45bdad69e6590ba878a3cda12c3a7cb6","abstract_canon_sha256":"e03163cd0931eb8f9ecf26baa78f1e9220a637b4ab415d66ee56c192b2a3778d"},"schema_version":"1.0"},"canonical_sha256":"e64942d8226b7d45f1341b597d99b07b2d004cf71a4c2244e4b985b24d35a07b","source":{"kind":"arxiv","id":"1803.01307","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.01307","created_at":"2026-05-18T00:20:01Z"},{"alias_kind":"arxiv_version","alias_value":"1803.01307v2","created_at":"2026-05-18T00:20:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01307","created_at":"2026-05-18T00:20:01Z"},{"alias_kind":"pith_short_12","alias_value":"4ZEUFWBCNN6U","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4ZEUFWBCNN6UL4JU","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4ZEUFWBC","created_at":"2026-05-18T12:32:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:4ZEUFWBCNN6UL4JUDNMX3GNQPM","target":"record","payload":{"canonical_record":{"source":{"id":"1803.01307","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-03-04T06:56:42Z","cross_cats_sorted":[],"title_canon_sha256":"7a2fc869111d22717ee8b8509790f26a45bdad69e6590ba878a3cda12c3a7cb6","abstract_canon_sha256":"e03163cd0931eb8f9ecf26baa78f1e9220a637b4ab415d66ee56c192b2a3778d"},"schema_version":"1.0"},"canonical_sha256":"e64942d8226b7d45f1341b597d99b07b2d004cf71a4c2244e4b985b24d35a07b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:01.795538Z","signature_b64":"dK4UWM/CLHNFctX6jungA7TILvPXvZ6s6FSN/jnvaHNpGrjyMIzQmd8RsyVkKUUmCfHkybx0SQpTyqkiKamLDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e64942d8226b7d45f1341b597d99b07b2d004cf71a4c2244e4b985b24d35a07b","last_reissued_at":"2026-05-18T00:20:01.794825Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:01.794825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.01307","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:20:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TSA3GTHRK077Le0DuH7BujykcWx2N6AplDY50l+sMhnU3oQ4q7eFWVlinoRTYRsHtBIQ6zzgZGqMh0qP7XkSDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:23:26.544759Z"},"content_sha256":"ff9457bb4ecc08fa1136805e634d09c9df38b54b9f3d5ea13c41e356964514c5","schema_version":"1.0","event_id":"sha256:ff9457bb4ecc08fa1136805e634d09c9df38b54b9f3d5ea13c41e356964514c5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:4ZEUFWBCNN6UL4JUDNMX3GNQPM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Angora: Efficient Fuzzing by Principled Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Hao Chen, Peng Chen","submitted_at":"2018-03-04T06:56:42Z","abstract_excerpt":"Fuzzing is a popular technique for finding software bugs. However, the performance of the state-of-the-art fuzzers leaves a lot to be desired. Fuzzers based on symbolic execution produce quality inputs but run slow, while fuzzers based on random mutation run fast but have difficulty producing quality inputs. We propose Angora, a new mutation-based fuzzer that outperforms the state-of-the-art fuzzers by a wide margin. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution. To solve path constraints efficiently, we introduce several key tech"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01307","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:20:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"msJE5wM5/Z7G3dSUY5rl1SoQiONfzT+57PREypopwA6UsA61iF/zk1UEBx90nN6PeGx/kzKaE6OQHtmOOqKKBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:23:26.545291Z"},"content_sha256":"aa99626d5e98ad865e8e32360020fa0ee9e9e0ebe705112542e7c05a477b7d48","schema_version":"1.0","event_id":"sha256:aa99626d5e98ad865e8e32360020fa0ee9e9e0ebe705112542e7c05a477b7d48"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4ZEUFWBCNN6UL4JUDNMX3GNQPM/bundle.json","state_url":"https://pith.science/pith/4ZEUFWBCNN6UL4JUDNMX3GNQPM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4ZEUFWBCNN6UL4JUDNMX3GNQPM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T10:23:26Z","links":{"resolver":"https://pith.science/pith/4ZEUFWBCNN6UL4JUDNMX3GNQPM","bundle":"https://pith.science/pith/4ZEUFWBCNN6UL4JUDNMX3GNQPM/bundle.json","state":"https://pith.science/pith/4ZEUFWBCNN6UL4JUDNMX3GNQPM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4ZEUFWBCNN6UL4JUDNMX3GNQPM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:4ZEUFWBCNN6UL4JUDNMX3GNQPM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e03163cd0931eb8f9ecf26baa78f1e9220a637b4ab415d66ee56c192b2a3778d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-03-04T06:56:42Z","title_canon_sha256":"7a2fc869111d22717ee8b8509790f26a45bdad69e6590ba878a3cda12c3a7cb6"},"schema_version":"1.0","source":{"id":"1803.01307","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.01307","created_at":"2026-05-18T00:20:01Z"},{"alias_kind":"arxiv_version","alias_value":"1803.01307v2","created_at":"2026-05-18T00:20:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01307","created_at":"2026-05-18T00:20:01Z"},{"alias_kind":"pith_short_12","alias_value":"4ZEUFWBCNN6U","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4ZEUFWBCNN6UL4JU","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4ZEUFWBC","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:aa99626d5e98ad865e8e32360020fa0ee9e9e0ebe705112542e7c05a477b7d48","target":"graph","created_at":"2026-05-18T00:20:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Fuzzing is a popular technique for finding software bugs. However, the performance of the state-of-the-art fuzzers leaves a lot to be desired. Fuzzers based on symbolic execution produce quality inputs but run slow, while fuzzers based on random mutation run fast but have difficulty producing quality inputs. We propose Angora, a new mutation-based fuzzer that outperforms the state-of-the-art fuzzers by a wide margin. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution. To solve path constraints efficiently, we introduce several key tech","authors_text":"Hao Chen, Peng Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-03-04T06:56:42Z","title":"Angora: Efficient Fuzzing by Principled Search"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01307","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ff9457bb4ecc08fa1136805e634d09c9df38b54b9f3d5ea13c41e356964514c5","target":"record","created_at":"2026-05-18T00:20:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e03163cd0931eb8f9ecf26baa78f1e9220a637b4ab415d66ee56c192b2a3778d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-03-04T06:56:42Z","title_canon_sha256":"7a2fc869111d22717ee8b8509790f26a45bdad69e6590ba878a3cda12c3a7cb6"},"schema_version":"1.0","source":{"id":"1803.01307","kind":"arxiv","version":2}},"canonical_sha256":"e64942d8226b7d45f1341b597d99b07b2d004cf71a4c2244e4b985b24d35a07b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e64942d8226b7d45f1341b597d99b07b2d004cf71a4c2244e4b985b24d35a07b","first_computed_at":"2026-05-18T00:20:01.794825Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:20:01.794825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dK4UWM/CLHNFctX6jungA7TILvPXvZ6s6FSN/jnvaHNpGrjyMIzQmd8RsyVkKUUmCfHkybx0SQpTyqkiKamLDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:20:01.795538Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.01307","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ff9457bb4ecc08fa1136805e634d09c9df38b54b9f3d5ea13c41e356964514c5","sha256:aa99626d5e98ad865e8e32360020fa0ee9e9e0ebe705112542e7c05a477b7d48"],"state_sha256":"656401ba2cc74f024720f37f18bc85a771d24643b41aef395f75e94fef20f7d2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PEZ7t6VX7tUom4WPJOt76lNUQTcucvwupaUeXlLP7fpaXwdeZ9e6cLXr3Dn7hUGHmGQI3CZfTUYf7LknOJv1Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T10:23:26.547992Z","bundle_sha256":"63a8044480e5520ac78460991113bcd268c4568422e5a75d8e5487bc38578437"}}