{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VWUJ2VW74E63ZYTJF2BFWFFSCX","short_pith_number":"pith:VWUJ2VW7","schema_version":"1.0","canonical_sha256":"ada89d56dfe13dbce2692e825b14b215ebf5204ccd9575ce0fbd9777c800dbf0","source":{"kind":"arxiv","id":"2505.18889","version":5},"attestation_state":"computed","paper":{"title":"Security Concerns for Large Language Models: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Benjamin C. M. Fung, Miles Q. Li","submitted_at":"2025-05-24T22:22:43Z","abstract_excerpt":"Large Language Models (LLMs) such as ChatGPT and its competitors have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. This survey provides a comprehensive overview of these emerging concerns, categorizing threats into several key areas: inference-time attacks via prompt manipulation; training-time attacks; misuse by malicious actors; and the inherent risks in autonomous LLM agents. Recently, a significant focus is increasingly being placed on the latter. We summarize recent academic and industrial studies from 2022 to 2025"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.18889","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-05-24T22:22:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8313838b85aca98434744372a23a929a94abab7f4e52fb1954c5b49ae998874a","abstract_canon_sha256":"f47609f953a367ee41b95d5eb1a7bb8ce4fa2ede6eea93d9dd5358b2ba33f164"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:29.915377Z","signature_b64":"9SZQJNSbka8yiNDRKZ6RAvhT39EotGfBKbpEhldhU7ksHaxzT1HNhebUoVDpnyMuaPlAka2lRealnWyZrj4yDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ada89d56dfe13dbce2692e825b14b215ebf5204ccd9575ce0fbd9777c800dbf0","last_reissued_at":"2026-07-05T11:58:29.914879Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:29.914879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Security Concerns for Large Language Models: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Benjamin C. M. Fung, Miles Q. Li","submitted_at":"2025-05-24T22:22:43Z","abstract_excerpt":"Large Language Models (LLMs) such as ChatGPT and its competitors have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. This survey provides a comprehensive overview of these emerging concerns, categorizing threats into several key areas: inference-time attacks via prompt manipulation; training-time attacks; misuse by malicious actors; and the inherent risks in autonomous LLM agents. Recently, a significant focus is increasingly being placed on the latter. We summarize recent academic and industrial studies from 2022 to 2025"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18889","kind":"arxiv","version":5},"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/2505.18889/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.18889","created_at":"2026-07-05T11:58:29.914937+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.18889v5","created_at":"2026-07-05T11:58:29.914937+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18889","created_at":"2026-07-05T11:58:29.914937+00:00"},{"alias_kind":"pith_short_12","alias_value":"VWUJ2VW74E63","created_at":"2026-07-05T11:58:29.914937+00:00"},{"alias_kind":"pith_short_16","alias_value":"VWUJ2VW74E63ZYTJ","created_at":"2026-07-05T11:58:29.914937+00:00"},{"alias_kind":"pith_short_8","alias_value":"VWUJ2VW7","created_at":"2026-07-05T11:58:29.914937+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.05929","citing_title":"LLM Harms: A Taxonomy and Discussion","ref_index":130,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18803","citing_title":"LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VWUJ2VW74E63ZYTJF2BFWFFSCX","json":"https://pith.science/pith/VWUJ2VW74E63ZYTJF2BFWFFSCX.json","graph_json":"https://pith.science/api/pith-number/VWUJ2VW74E63ZYTJF2BFWFFSCX/graph.json","events_json":"https://pith.science/api/pith-number/VWUJ2VW74E63ZYTJF2BFWFFSCX/events.json","paper":"https://pith.science/paper/VWUJ2VW7"},"agent_actions":{"view_html":"https://pith.science/pith/VWUJ2VW74E63ZYTJF2BFWFFSCX","download_json":"https://pith.science/pith/VWUJ2VW74E63ZYTJF2BFWFFSCX.json","view_paper":"https://pith.science/paper/VWUJ2VW7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.18889&json=true","fetch_graph":"https://pith.science/api/pith-number/VWUJ2VW74E63ZYTJF2BFWFFSCX/graph.json","fetch_events":"https://pith.science/api/pith-number/VWUJ2VW74E63ZYTJF2BFWFFSCX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VWUJ2VW74E63ZYTJF2BFWFFSCX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VWUJ2VW74E63ZYTJF2BFWFFSCX/action/storage_attestation","attest_author":"https://pith.science/pith/VWUJ2VW74E63ZYTJF2BFWFFSCX/action/author_attestation","sign_citation":"https://pith.science/pith/VWUJ2VW74E63ZYTJF2BFWFFSCX/action/citation_signature","submit_replication":"https://pith.science/pith/VWUJ2VW74E63ZYTJF2BFWFFSCX/action/replication_record"}},"created_at":"2026-07-05T11:58:29.914937+00:00","updated_at":"2026-07-05T11:58:29.914937+00:00"}