{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:4YDCZD3HIFAGGUF2GNFPTQWT5Q","short_pith_number":"pith:4YDCZD3H","canonical_record":{"source":{"id":"2605.12746","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-12T20:49:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b9c627c4b0c3b0fabbbd240dd7bcdf97f55865f8c664e33f0300c3275e2e0346","abstract_canon_sha256":"db7479902aa54671ef95bb226d3eefdaa69531f5d9147104af5384e1211ea622"},"schema_version":"1.0"},"canonical_sha256":"e6062c8f6741406350ba334af9c2d3ec3e582a751b36155163753a68700723b6","source":{"kind":"arxiv","id":"2605.12746","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12746","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12746v1","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12746","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"pith_short_12","alias_value":"4YDCZD3HIFAG","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"4YDCZD3HIFAGGUF2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"4YDCZD3H","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:4YDCZD3HIFAGGUF2GNFPTQWT5Q","target":"record","payload":{"canonical_record":{"source":{"id":"2605.12746","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-12T20:49:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b9c627c4b0c3b0fabbbd240dd7bcdf97f55865f8c664e33f0300c3275e2e0346","abstract_canon_sha256":"db7479902aa54671ef95bb226d3eefdaa69531f5d9147104af5384e1211ea622"},"schema_version":"1.0"},"canonical_sha256":"e6062c8f6741406350ba334af9c2d3ec3e582a751b36155163753a68700723b6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:48.992726Z","signature_b64":"Kb9s68hPux+P75XmldHVX3iB/QLYyXfNCd2OMld8IDzguvJ1HJUUNJXSlapB4M0Lk3xURXbPLc1mdte//1CUDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6062c8f6741406350ba334af9c2d3ec3e582a751b36155163753a68700723b6","last_reissued_at":"2026-05-18T03:09:48.991883Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:48.991883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.12746","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":"2026-05-18T03:09:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nQldQtWHuMFfwM9IrZdQJUPtLQf7Q5NDGmT3NBm887LA//i5ORcXDS8bv5oAcUysaW1OL6+ackfLQWt1EKVqDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T17:22:41.301885Z"},"content_sha256":"95ef26543864c54439377161bc65522ca2a1f4962543a4ba191fab16ca7cce1a","schema_version":"1.0","event_id":"sha256:95ef26543864c54439377161bc65522ca2a1f4962543a4ba191fab16ca7cce1a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:4YDCZD3HIFAGGUF2GNFPTQWT5Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CoT-Guard: Small Models for Strong Monitoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A 4B model trained with SFT and RL monitors chain-of-thought to detect hidden objectives better than several larger models.","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Berkcan Kapusuzoglu, Gang Wang, Giri Iyengar, Han Wang, Huan Zhang, Nirav Diwan, Ramin Moradi, Sambit Sahu, Supriyo Chakraborty","submitted_at":"2026-05-12T20:49:30Z","abstract_excerpt":"Monitoring the chain-of-thought (CoT) of reasoning models is a promising approach for detecting covert misbehavior (i.e., hidden objectives) in code generation tasks. While large models (GPT-5, Gemini-3-Flash) can serve as effective CoT monitors, they are expensive to deploy due to the lengthy reasoning traces and high API cost, emphasizing the need for smaller, cheaper alternatives. Nevertheless, we find that current small models (4B--8B) struggle to detect hidden objectives despite access to the CoT, frequently misattributing them as part of the user query. To address this, we propose a post"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"we introduce CoT-Guard, a 4B-parameter monitor that demonstrates superior generalization performance under both prompt and code manipulation attacks, achieving a G-mean^2 (i.e., TNR x TPR) of 75% and outperforming GPT-5.4 (56%), GPT-5-mini (41%), and Qwen3-32B (54%), while closing the gap to Gemini-3-Flash (83%).","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That reinforcement learning on subtly crafted hidden objectives will generalize to realistic third-party LLM router attacks in supply-chain scenarios without overfitting to the specific training distribution.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CoT-Guard is a 4B model using SFT and RL that achieves 75% G-mean^2 on hidden objective detection under prompt and code manipulation attacks, outperforming several larger models.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A 4B model trained with SFT and RL monitors chain-of-thought to detect hidden objectives better than several larger models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3174a88787929a692605b446eb5ebdccf4117e1095f82ff72f67d09c67f3f323"},"source":{"id":"2605.12746","kind":"arxiv","version":1},"verdict":{"id":"e3b37f53-52d0-40d8-a930-138b330a449c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T19:34:10.728125Z","strongest_claim":"we introduce CoT-Guard, a 4B-parameter monitor that demonstrates superior generalization performance under both prompt and code manipulation attacks, achieving a G-mean^2 (i.e., TNR x TPR) of 75% and outperforming GPT-5.4 (56%), GPT-5-mini (41%), and Qwen3-32B (54%), while closing the gap to Gemini-3-Flash (83%).","one_line_summary":"CoT-Guard is a 4B model using SFT and RL that achieves 75% G-mean^2 on hidden objective detection under prompt and code manipulation attacks, outperforming several larger models.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That reinforcement learning on subtly crafted hidden objectives will generalize to realistic third-party LLM router attacks in supply-chain scenarios without overfitting to the specific training distribution.","pith_extraction_headline":"A 4B model trained with SFT and RL monitors chain-of-thought to detect hidden objectives better than several larger models."},"references":{"count":67,"sample":[{"doi":"","year":2026,"title":"Anthropic. Claude Code. https://www.anthropic.com/product/claude- code . Accessed: 2026-05-07","work_id":"903d24b2-d4b0-49ab-a134-37b48fe19db1","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Devin: The AI Software Engineer.https://devin.ai/","work_id":"5830fe55-2002-4ae6-91d5-688f94b35cdf","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Swe-agent: Agent-computer interfaces enable automated software engineering","work_id":"a48ea5ef-a60f-4040-983a-3545f39b60a1","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?","work_id":"d0effe15-a689-441a-8e3f-ea35f1c4e4b1","ref_index":4,"cited_arxiv_id":"2310.06770","is_internal_anchor":true},{"doi":"","year":2024,"title":"Huatong Song, Lisheng Huang, Shuang Sun, Jinhao Jiang, Ran Le, Daixuan Cheng, Guoxin Chen, Yiwen Hu, Zongchao Chen, Wayne Xin Zhao, and 1 oth- ers","work_id":"17189f19-7774-4b97-ab44-9966bf5d6d48","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":67,"snapshot_sha256":"b2c191c0425163d029663f58b221923b916847ce280ffd729169d113578bf641","internal_anchors":12},"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":"e3b37f53-52d0-40d8-a930-138b330a449c"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:09:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/K1xfGKWwV2hkuhtjvDgfde1Jv5dCyBxxlXQm/GpC0E/UtAilI2s658xF7sFq4m4vBcYsFADkxK3134xEDy/Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T17:22:41.302943Z"},"content_sha256":"97530a7f24ec873518a37864efe91d05b19a6827d5dc601effe291b21b078b37","schema_version":"1.0","event_id":"sha256:97530a7f24ec873518a37864efe91d05b19a6827d5dc601effe291b21b078b37"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4YDCZD3HIFAGGUF2GNFPTQWT5Q/bundle.json","state_url":"https://pith.science/pith/4YDCZD3HIFAGGUF2GNFPTQWT5Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4YDCZD3HIFAGGUF2GNFPTQWT5Q/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":"2026-05-30T17:22:41Z","links":{"resolver":"https://pith.science/pith/4YDCZD3HIFAGGUF2GNFPTQWT5Q","bundle":"https://pith.science/pith/4YDCZD3HIFAGGUF2GNFPTQWT5Q/bundle.json","state":"https://pith.science/pith/4YDCZD3HIFAGGUF2GNFPTQWT5Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4YDCZD3HIFAGGUF2GNFPTQWT5Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4YDCZD3HIFAGGUF2GNFPTQWT5Q","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":"db7479902aa54671ef95bb226d3eefdaa69531f5d9147104af5384e1211ea622","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-12T20:49:30Z","title_canon_sha256":"b9c627c4b0c3b0fabbbd240dd7bcdf97f55865f8c664e33f0300c3275e2e0346"},"schema_version":"1.0","source":{"id":"2605.12746","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12746","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12746v1","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12746","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"pith_short_12","alias_value":"4YDCZD3HIFAG","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"4YDCZD3HIFAGGUF2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"4YDCZD3H","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:97530a7f24ec873518a37864efe91d05b19a6827d5dc601effe291b21b078b37","target":"graph","created_at":"2026-05-18T03:09:48Z","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":"we introduce CoT-Guard, a 4B-parameter monitor that demonstrates superior generalization performance under both prompt and code manipulation attacks, achieving a G-mean^2 (i.e., TNR x TPR) of 75% and outperforming GPT-5.4 (56%), GPT-5-mini (41%), and Qwen3-32B (54%), while closing the gap to Gemini-3-Flash (83%)."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That reinforcement learning on subtly crafted hidden objectives will generalize to realistic third-party LLM router attacks in supply-chain scenarios without overfitting to the specific training distribution."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"CoT-Guard is a 4B model using SFT and RL that achieves 75% G-mean^2 on hidden objective detection under prompt and code manipulation attacks, outperforming several larger models."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A 4B model trained with SFT and RL monitors chain-of-thought to detect hidden objectives better than several larger models."}],"snapshot_sha256":"3174a88787929a692605b446eb5ebdccf4117e1095f82ff72f67d09c67f3f323"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Monitoring the chain-of-thought (CoT) of reasoning models is a promising approach for detecting covert misbehavior (i.e., hidden objectives) in code generation tasks. While large models (GPT-5, Gemini-3-Flash) can serve as effective CoT monitors, they are expensive to deploy due to the lengthy reasoning traces and high API cost, emphasizing the need for smaller, cheaper alternatives. Nevertheless, we find that current small models (4B--8B) struggle to detect hidden objectives despite access to the CoT, frequently misattributing them as part of the user query. To address this, we propose a post","authors_text":"Berkcan Kapusuzoglu, Gang Wang, Giri Iyengar, Han Wang, Huan Zhang, Nirav Diwan, Ramin Moradi, Sambit Sahu, Supriyo Chakraborty","cross_cats":["cs.AI"],"headline":"A 4B model trained with SFT and RL monitors chain-of-thought to detect hidden objectives better than several larger models.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-12T20:49:30Z","title":"CoT-Guard: Small Models for Strong Monitoring"},"references":{"count":67,"internal_anchors":12,"resolved_work":67,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Anthropic. Claude Code. https://www.anthropic.com/product/claude- code . Accessed: 2026-05-07","work_id":"903d24b2-d4b0-49ab-a134-37b48fe19db1","year":2026},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Devin: The AI Software Engineer.https://devin.ai/","work_id":"5830fe55-2002-4ae6-91d5-688f94b35cdf","year":2026},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Swe-agent: Agent-computer interfaces enable automated software engineering","work_id":"a48ea5ef-a60f-4040-983a-3545f39b60a1","year":2024},{"cited_arxiv_id":"2310.06770","doi":"","is_internal_anchor":true,"ref_index":4,"title":"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?","work_id":"d0effe15-a689-441a-8e3f-ea35f1c4e4b1","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Huatong Song, Lisheng Huang, Shuang Sun, Jinhao Jiang, Ran Le, Daixuan Cheng, Guoxin Chen, Yiwen Hu, Zongchao Chen, Wayne Xin Zhao, and 1 oth- ers","work_id":"17189f19-7774-4b97-ab44-9966bf5d6d48","year":2024}],"snapshot_sha256":"b2c191c0425163d029663f58b221923b916847ce280ffd729169d113578bf641"},"source":{"id":"2605.12746","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T19:34:10.728125Z","id":"e3b37f53-52d0-40d8-a930-138b330a449c","model_set":{"reader":"grok-4.3"},"one_line_summary":"CoT-Guard is a 4B model using SFT and RL that achieves 75% G-mean^2 on hidden objective detection under prompt and code manipulation attacks, outperforming several larger models.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A 4B model trained with SFT and RL monitors chain-of-thought to detect hidden objectives better than several larger models.","strongest_claim":"we introduce CoT-Guard, a 4B-parameter monitor that demonstrates superior generalization performance under both prompt and code manipulation attacks, achieving a G-mean^2 (i.e., TNR x TPR) of 75% and outperforming GPT-5.4 (56%), GPT-5-mini (41%), and Qwen3-32B (54%), while closing the gap to Gemini-3-Flash (83%).","weakest_assumption":"That reinforcement learning on subtly crafted hidden objectives will generalize to realistic third-party LLM router attacks in supply-chain scenarios without overfitting to the specific training distribution."}},"verdict_id":"e3b37f53-52d0-40d8-a930-138b330a449c"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:95ef26543864c54439377161bc65522ca2a1f4962543a4ba191fab16ca7cce1a","target":"record","created_at":"2026-05-18T03:09:48Z","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":"db7479902aa54671ef95bb226d3eefdaa69531f5d9147104af5384e1211ea622","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-12T20:49:30Z","title_canon_sha256":"b9c627c4b0c3b0fabbbd240dd7bcdf97f55865f8c664e33f0300c3275e2e0346"},"schema_version":"1.0","source":{"id":"2605.12746","kind":"arxiv","version":1}},"canonical_sha256":"e6062c8f6741406350ba334af9c2d3ec3e582a751b36155163753a68700723b6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e6062c8f6741406350ba334af9c2d3ec3e582a751b36155163753a68700723b6","first_computed_at":"2026-05-18T03:09:48.991883Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:48.991883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Kb9s68hPux+P75XmldHVX3iB/QLYyXfNCd2OMld8IDzguvJ1HJUUNJXSlapB4M0Lk3xURXbPLc1mdte//1CUDw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:48.992726Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12746","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:95ef26543864c54439377161bc65522ca2a1f4962543a4ba191fab16ca7cce1a","sha256:97530a7f24ec873518a37864efe91d05b19a6827d5dc601effe291b21b078b37"],"state_sha256":"86e2dcd97c7c96caf1a6102ac562e98a116ea06b36955940701ba4edaad8472c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6TQe7b25qapNsQrKLQmSOynBSKf8DL2Cc0Ji+u62vYNJnoLbI8r4wBYVipbtjW+0qr/MP9HjwHAVgFzfUsw+Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T17:22:41.307614Z","bundle_sha256":"b6fc759e5536e952ef98f44f4013ec4c4d09a0abb493daafa3901ca70cad9dee"}}