{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MKMMPNKKJKLBZRICFL4K3ERRZE","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":"afce5cf9b31ff164c4576efb396c9be074a14312a10616e02f63c297c3636b68","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-12-27T01:00:23Z","title_canon_sha256":"7d107c7737121f8687342eee8bc908b8a0a5292d1b6e3b4640548fed2b3fc0f9"},"schema_version":"1.0","source":{"id":"2412.19394","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.19394","created_at":"2026-07-05T10:13:45Z"},{"alias_kind":"arxiv_version","alias_value":"2412.19394v2","created_at":"2026-07-05T10:13:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19394","created_at":"2026-07-05T10:13:45Z"},{"alias_kind":"pith_short_12","alias_value":"MKMMPNKKJKLB","created_at":"2026-07-05T10:13:45Z"},{"alias_kind":"pith_short_16","alias_value":"MKMMPNKKJKLBZRIC","created_at":"2026-07-05T10:13:45Z"},{"alias_kind":"pith_short_8","alias_value":"MKMMPNKK","created_at":"2026-07-05T10:13:45Z"}],"graph_snapshots":[{"event_id":"sha256:00ebce35d8130bf36882253b4118763b6f7a2a3b97acbc1266569fb6c9d3b061","target":"graph","created_at":"2026-07-05T10:13:45Z","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/2412.19394/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Auto-regressive large language models (LLMs) have yielded impressive performance in many real-world tasks. However, the new paradigm of these LLMs also exposes novel threats. In this paper, we explore their vulnerability to inference cost attacks, where a malicious user crafts Engorgio prompts to intentionally increase the computation cost and latency of the inference process. We design Engorgio, a novel methodology, to efficiently generate adversarial Engorgio prompts to affect the target LLM's service availability. Engorgio has the following two technical contributions. (1) We employ a param","authors_text":"Chao Zhang, Han Qiu, Hao Wang, Hewu Li, Jianshuo Dong, Ke Xu, Qi Li, Qingjie Zhang, Tianwei Zhang, Ziyuan Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-12-27T01:00:23Z","title":"An Engorgio Prompt Makes Large Language Model Babble on"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19394","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:cf41cb52e4d705b19bbaf8556b1f8377ec392c77ce33e2ba8c7e0e4e62330f6d","target":"record","created_at":"2026-07-05T10:13:45Z","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":"afce5cf9b31ff164c4576efb396c9be074a14312a10616e02f63c297c3636b68","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-12-27T01:00:23Z","title_canon_sha256":"7d107c7737121f8687342eee8bc908b8a0a5292d1b6e3b4640548fed2b3fc0f9"},"schema_version":"1.0","source":{"id":"2412.19394","kind":"arxiv","version":2}},"canonical_sha256":"6298c7b54a4a961cc5022af8ad9231c923b3df5594d2afb7fbb7dac9c8f47f3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6298c7b54a4a961cc5022af8ad9231c923b3df5594d2afb7fbb7dac9c8f47f3f","first_computed_at":"2026-07-05T10:13:45.965764Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:13:45.965764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vCl0gMg6d9Y6gRuEjOzLSf4MsleMZxKP6/u6IKi1zlKQEjV616v08NAPHtBDqgeFC8CRQhAkBO9SUhUB7Q3ADw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:13:45.966266Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.19394","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf41cb52e4d705b19bbaf8556b1f8377ec392c77ce33e2ba8c7e0e4e62330f6d","sha256:00ebce35d8130bf36882253b4118763b6f7a2a3b97acbc1266569fb6c9d3b061"],"state_sha256":"42f998d7123a053046c985b53e44e439378400436815d6a495d9d1843df9559b"}