{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:73IXOQ3CGNUZCC7HFAZWJNJX6P","short_pith_number":"pith:73IXOQ3C","canonical_record":{"source":{"id":"2311.02105","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T09:18:21Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"518953c7f6992391d722b5a752d67255d6e89009fde37fc024761f71749eccfb","abstract_canon_sha256":"6cda2cb21301cef78bc56ad90670d2b2b177086e8e478a830c0060158e585f96"},"schema_version":"1.0"},"canonical_sha256":"fed17743623369910be7283364b537f3c1cd23a491afeeb08080c6a5f840f9a7","source":{"kind":"arxiv","id":"2311.02105","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.02105","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"arxiv_version","alias_value":"2311.02105v1","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.02105","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"pith_short_12","alias_value":"73IXOQ3CGNUZ","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"pith_short_16","alias_value":"73IXOQ3CGNUZCC7H","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"pith_short_8","alias_value":"73IXOQ3C","created_at":"2026-07-05T07:09:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:73IXOQ3CGNUZCC7HFAZWJNJX6P","target":"record","payload":{"canonical_record":{"source":{"id":"2311.02105","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T09:18:21Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"518953c7f6992391d722b5a752d67255d6e89009fde37fc024761f71749eccfb","abstract_canon_sha256":"6cda2cb21301cef78bc56ad90670d2b2b177086e8e478a830c0060158e585f96"},"schema_version":"1.0"},"canonical_sha256":"fed17743623369910be7283364b537f3c1cd23a491afeeb08080c6a5f840f9a7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:09:33.281185Z","signature_b64":"QrEUfEOkBqCoKh8mX4dezvnpn+9naRDHOB0IFhilwlwqyJlC2jcmHEcj+I+qtUJTkHJxZsT7gbw3Sh63kC5WBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fed17743623369910be7283364b537f3c1cd23a491afeeb08080c6a5f840f9a7","last_reissued_at":"2026-07-05T07:09:33.280712Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:09:33.280712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.02105","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:09:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QZTmDqg02WBifumP2it4jWcY+4khHkKkjNG44HLmpcFSeB1lP5boPZRQnX9DrfFZF4QWxdIzg0kAdataS73tBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:21:48.166556Z"},"content_sha256":"75b3a4bb2c924fb60e9bd073a9a1b128a8cd0465ff6032935a47d5595c3dc5e5","schema_version":"1.0","event_id":"sha256:75b3a4bb2c924fb60e9bd073a9a1b128a8cd0465ff6032935a47d5595c3dc5e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:73IXOQ3CGNUZCC7HFAZWJNJX6P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Making Harmful Behaviors Unlearnable for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.LG","authors_text":"Qi Zhang, Ruotian Ma, Tao Gui, Xin Zhou, Xuanjing Huang, Yi Lu","submitted_at":"2023-11-02T09:18:21Z","abstract_excerpt":"Large language models (LLMs) have shown great potential as general-purpose AI assistants in various domains. To meet the requirements of different applications, LLMs are often customized by further fine-tuning. However, the powerful learning ability of LLMs not only enables them to acquire new tasks but also makes them susceptible to learning undesired behaviors. For example, even safety-aligned LLMs can be easily fine-tuned into harmful assistants as the fine-tuning data often contains implicit or explicit harmful content. Can we train LLMs on harmful data without learning harmful behaviors? "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.02105","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/2311.02105/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:09:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1Cb+mXTJgaSdfbM/EPGVmn+vqfaDQoPnKRh37+6YAYel4eP5QEzZ33JFvXtYMdOK5yHdKcPbbCI8iwS30DarAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:21:48.167118Z"},"content_sha256":"9c01e59db3668fabcc6d1fe608609c038d5f177f9aab416c33ea745869b52be1","schema_version":"1.0","event_id":"sha256:9c01e59db3668fabcc6d1fe608609c038d5f177f9aab416c33ea745869b52be1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/73IXOQ3CGNUZCC7HFAZWJNJX6P/bundle.json","state_url":"https://pith.science/pith/73IXOQ3CGNUZCC7HFAZWJNJX6P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/73IXOQ3CGNUZCC7HFAZWJNJX6P/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":"2026-08-12T22:21:48Z","links":{"resolver":"https://pith.science/pith/73IXOQ3CGNUZCC7HFAZWJNJX6P","bundle":"https://pith.science/pith/73IXOQ3CGNUZCC7HFAZWJNJX6P/bundle.json","state":"https://pith.science/pith/73IXOQ3CGNUZCC7HFAZWJNJX6P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/73IXOQ3CGNUZCC7HFAZWJNJX6P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:73IXOQ3CGNUZCC7HFAZWJNJX6P","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":"6cda2cb21301cef78bc56ad90670d2b2b177086e8e478a830c0060158e585f96","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T09:18:21Z","title_canon_sha256":"518953c7f6992391d722b5a752d67255d6e89009fde37fc024761f71749eccfb"},"schema_version":"1.0","source":{"id":"2311.02105","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.02105","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"arxiv_version","alias_value":"2311.02105v1","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.02105","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"pith_short_12","alias_value":"73IXOQ3CGNUZ","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"pith_short_16","alias_value":"73IXOQ3CGNUZCC7H","created_at":"2026-07-05T07:09:33Z"},{"alias_kind":"pith_short_8","alias_value":"73IXOQ3C","created_at":"2026-07-05T07:09:33Z"}],"graph_snapshots":[{"event_id":"sha256:9c01e59db3668fabcc6d1fe608609c038d5f177f9aab416c33ea745869b52be1","target":"graph","created_at":"2026-07-05T07:09:33Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2311.02105/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have shown great potential as general-purpose AI assistants in various domains. To meet the requirements of different applications, LLMs are often customized by further fine-tuning. However, the powerful learning ability of LLMs not only enables them to acquire new tasks but also makes them susceptible to learning undesired behaviors. For example, even safety-aligned LLMs can be easily fine-tuned into harmful assistants as the fine-tuning data often contains implicit or explicit harmful content. Can we train LLMs on harmful data without learning harmful behaviors? ","authors_text":"Qi Zhang, Ruotian Ma, Tao Gui, Xin Zhou, Xuanjing Huang, Yi Lu","cross_cats":["cs.AI","cs.CY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T09:18:21Z","title":"Making Harmful Behaviors Unlearnable for Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.02105","kind":"arxiv","version":1},"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":"sha256:75b3a4bb2c924fb60e9bd073a9a1b128a8cd0465ff6032935a47d5595c3dc5e5","target":"record","created_at":"2026-07-05T07:09:33Z","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":"6cda2cb21301cef78bc56ad90670d2b2b177086e8e478a830c0060158e585f96","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T09:18:21Z","title_canon_sha256":"518953c7f6992391d722b5a752d67255d6e89009fde37fc024761f71749eccfb"},"schema_version":"1.0","source":{"id":"2311.02105","kind":"arxiv","version":1}},"canonical_sha256":"fed17743623369910be7283364b537f3c1cd23a491afeeb08080c6a5f840f9a7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fed17743623369910be7283364b537f3c1cd23a491afeeb08080c6a5f840f9a7","first_computed_at":"2026-07-05T07:09:33.280712Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:09:33.280712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QrEUfEOkBqCoKh8mX4dezvnpn+9naRDHOB0IFhilwlwqyJlC2jcmHEcj+I+qtUJTkHJxZsT7gbw3Sh63kC5WBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:09:33.281185Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.02105","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75b3a4bb2c924fb60e9bd073a9a1b128a8cd0465ff6032935a47d5595c3dc5e5","sha256:9c01e59db3668fabcc6d1fe608609c038d5f177f9aab416c33ea745869b52be1"],"state_sha256":"96e767bd5aeed24f6a49b5529af9e7802cff3f80fd5e74f12b40b3a58db1cb7e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lYZ2/iO55WHUkp9CrfaJOfHcd62XR3wmm6+2C2RoFnCbFYHGiwgIrrjS610o+Va9ftFvHKIrHBSGGkWgp6x8Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T22:21:48.188392Z","bundle_sha256":"5ca9a2bc7c923fe6c041ed99278d4280faaebaaae4ca11ea06d4b58b124f3a07"}}