{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:4UGGJIUCOLSCTUSUOEVORCCCXJ","short_pith_number":"pith:4UGGJIUC","canonical_record":{"source":{"id":"2307.15043","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-27T17:49:12Z","cross_cats_sorted":["cs.AI","cs.CR","cs.LG"],"title_canon_sha256":"67a5a31016d9736acd896eb07a14f8ec98cc13ce3eb192fff40153614e5d90cd","abstract_canon_sha256":"ccb84d942f4878eac6794dd48cde66615974642ba1af148cb6c048d0d509cf7b"},"schema_version":"1.0"},"canonical_sha256":"e50c64a28272e429d254712ae88842ba5071a8b2d9bc3bd439d98328930c7405","source":{"kind":"arxiv","id":"2307.15043","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.15043","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"arxiv_version","alias_value":"2307.15043v2","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.15043","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"pith_short_12","alias_value":"4UGGJIUCOLSC","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"pith_short_16","alias_value":"4UGGJIUCOLSCTUSU","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"pith_short_8","alias_value":"4UGGJIUC","created_at":"2026-07-05T07:26:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:4UGGJIUCOLSCTUSUOEVORCCCXJ","target":"record","payload":{"canonical_record":{"source":{"id":"2307.15043","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-27T17:49:12Z","cross_cats_sorted":["cs.AI","cs.CR","cs.LG"],"title_canon_sha256":"67a5a31016d9736acd896eb07a14f8ec98cc13ce3eb192fff40153614e5d90cd","abstract_canon_sha256":"ccb84d942f4878eac6794dd48cde66615974642ba1af148cb6c048d0d509cf7b"},"schema_version":"1.0"},"canonical_sha256":"e50c64a28272e429d254712ae88842ba5071a8b2d9bc3bd439d98328930c7405","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:26:38.871610Z","signature_b64":"07mFpmtn219iTe/So555cTHfqEE9/sek2Hj2Np/QNOk2DJChZM9vS2jMseX8QMdcxQ1VAZdM4ZSUFxMQsNpbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e50c64a28272e429d254712ae88842ba5071a8b2d9bc3bd439d98328930c7405","last_reissued_at":"2026-07-05T07:26:38.871121Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:26:38.871121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.15043","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":"2026-07-05T07:26:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jd5VfrnqdS4qZy9i6Q/ORb9IDJVxwuSOeouNkpJvZ5WbwvClyQeQfCHeRRpqTnEKggI3ZCsPMcYsLSs956hqCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:28:15.081213Z"},"content_sha256":"dac5606b8e56f7a4166b052b565952b2e7f58bd7d4c3868542d9fd8e3149f811","schema_version":"1.0","event_id":"sha256:dac5606b8e56f7a4166b052b565952b2e7f58bd7d4c3868542d9fd8e3149f811"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:4UGGJIUCOLSCTUSUOEVORCCCXJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"An automatically found adversarial suffix transfers to make aligned LLMs including ChatGPT generate objectionable content.","cross_cats":["cs.AI","cs.CR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Andy Zou, J. Zico Kolter, Matt Fredrikson, Milad Nasr, Nicholas Carlini, Zifan Wang","submitted_at":"2023-07-27T17:49:12Z","abstract_excerpt":"Because \"out-of-the-box\" large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an attempt to prevent undesirable generation. While there has been some success at circumventing these measures -- so-called \"jailbreaks\" against LLMs -- these attacks have required significant human ingenuity and are brittle in practice. In this paper, we propose a simple and effective attack method that causes aligned language models to generate objectionable behaviors. Specifically, our approach finds a suffix that, when attached"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"When the attack suffix is trained on multiple prompts and on Vicuna-7B and 13B, the resulting suffix induces objectionable content in the public interfaces to ChatGPT, Bard, and Claude, as well as open source LLMs such as LLaMA-2-Chat, Pythia, Falcon, and others.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The optimization procedure finds suffixes whose success on held-out prompts and unseen models is not explained by overfitting to the specific training queries and the two Vicuna models used during search.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Gradient and greedy search over token suffixes produces universal, transferable adversarial prompts that elicit objectionable outputs from aligned models including black-box commercial systems.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"An automatically found adversarial suffix transfers to make aligned LLMs including ChatGPT generate objectionable content.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2265cb5e8cad4cc8ca95fcf50c22d7c561022df9b979e7a77c4fd6b85f903b54"},"source":{"id":"2307.15043","kind":"arxiv","version":2},"verdict":{"id":"f340e1b0-9744-452f-b034-1dfb0708c574","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-24T07:39:45.512393Z","strongest_claim":"When the attack suffix is trained on multiple prompts and on Vicuna-7B and 13B, the resulting suffix induces objectionable content in the public interfaces to ChatGPT, Bard, and Claude, as well as open source LLMs such as LLaMA-2-Chat, Pythia, Falcon, and others.","one_line_summary":"Gradient and greedy search over token suffixes produces universal, transferable adversarial prompts that elicit objectionable outputs from aligned models including black-box commercial systems.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The optimization procedure finds suffixes whose success on held-out prompts and unseen models is not explained by overfitting to the specific training queries and the two Vicuna models used during search.","pith_extraction_headline":"An automatically found adversarial suffix transfers to make aligned LLMs including ChatGPT generate objectionable content."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2307.15043/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":29,"sample":[{"doi":"","year":null,"title":"Generating Natural Language Adversarial Examples","work_id":"f55a0f76-2c64-4349-91d9-cc64a1e0d033","ref_index":1,"cited_arxiv_id":"1804.07998","is_internal_anchor":true},{"doi":"","year":null,"title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","work_id":"a1f2574b-a899-4713-be60-c87ba332656c","ref_index":2,"cited_arxiv_id":"2204.05862","is_internal_anchor":true},{"doi":"","year":2013,"title":"Evasion attacks against machine learning at test time","work_id":"c42e0f74-6ecb-451a-9118-1dd241d5eb86","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Adversarial examples are not easily detected: Bypassing ten detection methods","work_id":"cbf9eae3-40c5-419a-9f5b-335eab5a2bb2","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"QLoRA: Efficient Finetuning of Quantized LLMs","work_id":"d3fdf68e-3a5e-48b5-8a18-7a9137479c55","ref_index":5,"cited_arxiv_id":"2305.14314","is_internal_anchor":true}],"resolved_work":29,"snapshot_sha256":"1b90b6d646c4aef51db005f0132f447d786c06b3e27b02105c7bfafac39ad64a","internal_anchors":15},"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":"f340e1b0-9744-452f-b034-1dfb0708c574"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:26:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"64SvXl2oC4ODdsR217YNEDTYLO4pru4AFeB9ktG5y7lfs1aVxtvekF4XMr6MR+NC3rMAmROwf4awIeYgmcRdBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:28:15.082445Z"},"content_sha256":"1af9151fa35504110aa12eb6ef02025c332597f16433b6d567c3f4a7be4a8fd9","schema_version":"1.0","event_id":"sha256:1af9151fa35504110aa12eb6ef02025c332597f16433b6d567c3f4a7be4a8fd9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4UGGJIUCOLSCTUSUOEVORCCCXJ/bundle.json","state_url":"https://pith.science/pith/4UGGJIUCOLSCTUSUOEVORCCCXJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4UGGJIUCOLSCTUSUOEVORCCCXJ/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":"2026-08-06T19:28:15Z","links":{"resolver":"https://pith.science/pith/4UGGJIUCOLSCTUSUOEVORCCCXJ","bundle":"https://pith.science/pith/4UGGJIUCOLSCTUSUOEVORCCCXJ/bundle.json","state":"https://pith.science/pith/4UGGJIUCOLSCTUSUOEVORCCCXJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4UGGJIUCOLSCTUSUOEVORCCCXJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:4UGGJIUCOLSCTUSUOEVORCCCXJ","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":"ccb84d942f4878eac6794dd48cde66615974642ba1af148cb6c048d0d509cf7b","cross_cats_sorted":["cs.AI","cs.CR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-27T17:49:12Z","title_canon_sha256":"67a5a31016d9736acd896eb07a14f8ec98cc13ce3eb192fff40153614e5d90cd"},"schema_version":"1.0","source":{"id":"2307.15043","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.15043","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"arxiv_version","alias_value":"2307.15043v2","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.15043","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"pith_short_12","alias_value":"4UGGJIUCOLSC","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"pith_short_16","alias_value":"4UGGJIUCOLSCTUSU","created_at":"2026-07-05T07:26:38Z"},{"alias_kind":"pith_short_8","alias_value":"4UGGJIUC","created_at":"2026-07-05T07:26:38Z"}],"graph_snapshots":[{"event_id":"sha256:1af9151fa35504110aa12eb6ef02025c332597f16433b6d567c3f4a7be4a8fd9","target":"graph","created_at":"2026-07-05T07:26:38Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"When the attack suffix is trained on multiple prompts and on Vicuna-7B and 13B, the resulting suffix induces objectionable content in the public interfaces to ChatGPT, Bard, and Claude, as well as open source LLMs such as LLaMA-2-Chat, Pythia, Falcon, and others."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The optimization procedure finds suffixes whose success on held-out prompts and unseen models is not explained by overfitting to the specific training queries and the two Vicuna models used during search."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Gradient and greedy search over token suffixes produces universal, transferable adversarial prompts that elicit objectionable outputs from aligned models including black-box commercial systems."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"An automatically found adversarial suffix transfers to make aligned LLMs including ChatGPT generate objectionable content."}],"snapshot_sha256":"2265cb5e8cad4cc8ca95fcf50c22d7c561022df9b979e7a77c4fd6b85f903b54"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2307.15043/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Because \"out-of-the-box\" large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an attempt to prevent undesirable generation. While there has been some success at circumventing these measures -- so-called \"jailbreaks\" against LLMs -- these attacks have required significant human ingenuity and are brittle in practice. In this paper, we propose a simple and effective attack method that causes aligned language models to generate objectionable behaviors. Specifically, our approach finds a suffix that, when attached","authors_text":"Andy Zou, J. Zico Kolter, Matt Fredrikson, Milad Nasr, Nicholas Carlini, Zifan Wang","cross_cats":["cs.AI","cs.CR","cs.LG"],"headline":"An automatically found adversarial suffix transfers to make aligned LLMs including ChatGPT generate objectionable content.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-27T17:49:12Z","title":"Universal and Transferable Adversarial Attacks on Aligned Language Models"},"references":{"count":29,"internal_anchors":15,"resolved_work":29,"sample":[{"cited_arxiv_id":"1804.07998","doi":"","is_internal_anchor":true,"ref_index":1,"title":"Generating Natural Language Adversarial Examples","work_id":"f55a0f76-2c64-4349-91d9-cc64a1e0d033","year":null},{"cited_arxiv_id":"2204.05862","doi":"","is_internal_anchor":true,"ref_index":2,"title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","work_id":"a1f2574b-a899-4713-be60-c87ba332656c","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Evasion attacks against machine learning at test time","work_id":"c42e0f74-6ecb-451a-9118-1dd241d5eb86","year":2013},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Adversarial examples are not easily detected: Bypassing ten detection methods","work_id":"cbf9eae3-40c5-419a-9f5b-335eab5a2bb2","year":null},{"cited_arxiv_id":"2305.14314","doi":"","is_internal_anchor":true,"ref_index":5,"title":"QLoRA: Efficient Finetuning of Quantized LLMs","work_id":"d3fdf68e-3a5e-48b5-8a18-7a9137479c55","year":null}],"snapshot_sha256":"1b90b6d646c4aef51db005f0132f447d786c06b3e27b02105c7bfafac39ad64a"},"source":{"id":"2307.15043","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-24T07:39:45.512393Z","id":"f340e1b0-9744-452f-b034-1dfb0708c574","model_set":{"reader":"grok-4.3"},"one_line_summary":"Gradient and greedy search over token suffixes produces universal, transferable adversarial prompts that elicit objectionable outputs from aligned models including black-box commercial systems.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"An automatically found adversarial suffix transfers to make aligned LLMs including ChatGPT generate objectionable content.","strongest_claim":"When the attack suffix is trained on multiple prompts and on Vicuna-7B and 13B, the resulting suffix induces objectionable content in the public interfaces to ChatGPT, Bard, and Claude, as well as open source LLMs such as LLaMA-2-Chat, Pythia, Falcon, and others.","weakest_assumption":"The optimization procedure finds suffixes whose success on held-out prompts and unseen models is not explained by overfitting to the specific training queries and the two Vicuna models used during search."}},"verdict_id":"f340e1b0-9744-452f-b034-1dfb0708c574"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:dac5606b8e56f7a4166b052b565952b2e7f58bd7d4c3868542d9fd8e3149f811","target":"record","created_at":"2026-07-05T07:26:38Z","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":"ccb84d942f4878eac6794dd48cde66615974642ba1af148cb6c048d0d509cf7b","cross_cats_sorted":["cs.AI","cs.CR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-27T17:49:12Z","title_canon_sha256":"67a5a31016d9736acd896eb07a14f8ec98cc13ce3eb192fff40153614e5d90cd"},"schema_version":"1.0","source":{"id":"2307.15043","kind":"arxiv","version":2}},"canonical_sha256":"e50c64a28272e429d254712ae88842ba5071a8b2d9bc3bd439d98328930c7405","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e50c64a28272e429d254712ae88842ba5071a8b2d9bc3bd439d98328930c7405","first_computed_at":"2026-07-05T07:26:38.871121Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:26:38.871121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"07mFpmtn219iTe/So555cTHfqEE9/sek2Hj2Np/QNOk2DJChZM9vS2jMseX8QMdcxQ1VAZdM4ZSUFxMQsNpbBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:26:38.871610Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.15043","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dac5606b8e56f7a4166b052b565952b2e7f58bd7d4c3868542d9fd8e3149f811","sha256:1af9151fa35504110aa12eb6ef02025c332597f16433b6d567c3f4a7be4a8fd9"],"state_sha256":"f7b8ea67b832e7c85e3a4b4050b0086e8539b988fac3347810b5ea976d701b02"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9C/m0pS6XStloKW84YsuhOvYNm1FD8/JAqkEp/52Zgbikwsi8Znl7QYfcwdF5rHXtzs7Jo+6nCMtBYMzrmiMCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T19:28:15.089140Z","bundle_sha256":"da4fcd316e0129eec7127a88d2ec81ed17c56c2bdcc80532a0d9a7251abf8c94"}}