{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BDWUEWPWNPWBND37S57JKA4YA3","short_pith_number":"pith:BDWUEWPW","schema_version":"1.0","canonical_sha256":"08ed4259f66bec168f7f977e95039806e119f6a307ed8fc19efc5ba5b12af91a","source":{"kind":"arxiv","id":"2401.07612","version":1},"attestation_state":"computed","paper":{"title":"Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Xuchen Suo","submitted_at":"2024-01-15T11:44:18Z","abstract_excerpt":"The critical challenge of prompt injection attacks in Large Language Models (LLMs) integrated applications, a growing concern in the Artificial Intelligence (AI) field. Such attacks, which manipulate LLMs through natural language inputs, pose a significant threat to the security of these applications. Traditional defense strategies, including output and input filtering, as well as delimiter use, have proven inadequate. This paper introduces the 'Signed-Prompt' method as a novel solution. The study involves signing sensitive instructions within command segments by authorized users, enabling the"},"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":"2401.07612","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2024-01-15T11:44:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1e212b6746cefe7b4eb6426fab98ab300b3ad45a240f8d771b5d3859803a5809","abstract_canon_sha256":"1d9106093023175c95f4dc101df394a653bdef27c69a8b6afe07d282e27b419a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:33:40.087704Z","signature_b64":"m6IrwRkAuOG9dmHuQPkGNZmprszggFi2ZTy7CXjiR+MsHNg4Uubq75BsVD5kfsoyNFUQokk6XjGr839YKP6/Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"08ed4259f66bec168f7f977e95039806e119f6a307ed8fc19efc5ba5b12af91a","last_reissued_at":"2026-07-05T07:33:40.087288Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:33:40.087288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Xuchen Suo","submitted_at":"2024-01-15T11:44:18Z","abstract_excerpt":"The critical challenge of prompt injection attacks in Large Language Models (LLMs) integrated applications, a growing concern in the Artificial Intelligence (AI) field. Such attacks, which manipulate LLMs through natural language inputs, pose a significant threat to the security of these applications. Traditional defense strategies, including output and input filtering, as well as delimiter use, have proven inadequate. This paper introduces the 'Signed-Prompt' method as a novel solution. The study involves signing sensitive instructions within command segments by authorized users, enabling the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.07612","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/2401.07612/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":"2401.07612","created_at":"2026-07-05T07:33:40.087353+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.07612v1","created_at":"2026-07-05T07:33:40.087353+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.07612","created_at":"2026-07-05T07:33:40.087353+00:00"},{"alias_kind":"pith_short_12","alias_value":"BDWUEWPWNPWB","created_at":"2026-07-05T07:33:40.087353+00:00"},{"alias_kind":"pith_short_16","alias_value":"BDWUEWPWNPWBND37","created_at":"2026-07-05T07:33:40.087353+00:00"},{"alias_kind":"pith_short_8","alias_value":"BDWUEWPW","created_at":"2026-07-05T07:33:40.087353+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.29055","citing_title":"Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23158","citing_title":"What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2504.20472","citing_title":"Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18133","citing_title":"An Empirical Study of Privacy Leakage Chains via Prompt Injection in Black-Box Chatbot Environments","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2510.23883","citing_title":"Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges","ref_index":186,"is_internal_anchor":false},{"citing_arxiv_id":"2410.07283","citing_title":"Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems","ref_index":103,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14604","citing_title":"Hijacking Large Audio-Language Models via Context-Agnostic and Imperceptible Auditory Prompt Injection","ref_index":63,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BDWUEWPWNPWBND37S57JKA4YA3","json":"https://pith.science/pith/BDWUEWPWNPWBND37S57JKA4YA3.json","graph_json":"https://pith.science/api/pith-number/BDWUEWPWNPWBND37S57JKA4YA3/graph.json","events_json":"https://pith.science/api/pith-number/BDWUEWPWNPWBND37S57JKA4YA3/events.json","paper":"https://pith.science/paper/BDWUEWPW"},"agent_actions":{"view_html":"https://pith.science/pith/BDWUEWPWNPWBND37S57JKA4YA3","download_json":"https://pith.science/pith/BDWUEWPWNPWBND37S57JKA4YA3.json","view_paper":"https://pith.science/paper/BDWUEWPW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.07612&json=true","fetch_graph":"https://pith.science/api/pith-number/BDWUEWPWNPWBND37S57JKA4YA3/graph.json","fetch_events":"https://pith.science/api/pith-number/BDWUEWPWNPWBND37S57JKA4YA3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BDWUEWPWNPWBND37S57JKA4YA3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BDWUEWPWNPWBND37S57JKA4YA3/action/storage_attestation","attest_author":"https://pith.science/pith/BDWUEWPWNPWBND37S57JKA4YA3/action/author_attestation","sign_citation":"https://pith.science/pith/BDWUEWPWNPWBND37S57JKA4YA3/action/citation_signature","submit_replication":"https://pith.science/pith/BDWUEWPWNPWBND37S57JKA4YA3/action/replication_record"}},"created_at":"2026-07-05T07:33:40.087353+00:00","updated_at":"2026-07-05T07:33:40.087353+00:00"}