{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HOVPBG4ETX4347WWXM3FKYXIHN","short_pith_number":"pith:HOVPBG4E","schema_version":"1.0","canonical_sha256":"3baaf09b849df9be7ed6bb365562e83b6780c6252c4b6dd0dd9c10964e828f38","source":{"kind":"arxiv","id":"2308.01990","version":4},"attestation_state":"computed","paper":{"title":"From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Daniel Castro, Nuno Santos, Paulo Carreira, Rodrigo Pedro","submitted_at":"2023-08-03T19:03:18Z","abstract_excerpt":"Large Language Models (LLMs) have found widespread applications in various domains, including web applications, where they facilitate human interaction via chatbots with natural language interfaces. Internally, aided by an LLM-integration middleware such as Langchain, user prompts are translated into SQL queries used by the LLM to provide meaningful responses to users. However, unsanitized user prompts can lead to SQL injection attacks, potentially compromising the security of the database. Despite the growing interest in prompt injection vulnerabilities targeting LLMs, the specific risks of g"},"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":"2308.01990","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-08-03T19:03:18Z","cross_cats_sorted":[],"title_canon_sha256":"34ce49bd4b5cb641573af3929d92ce00b11ba1fb959433b8bdcb199710e1a66e","abstract_canon_sha256":"f952f8877f3d1f3fb9fddfb488b0d015a211902b2c4c8062321f66a51c4ec301"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:01.394099Z","signature_b64":"zqUfMkPXmmGGSgwPggpzKASHNa6yzG+PTWz7FJbIPuhWGxD4R4nGk5Hz0vJzM5wU4l+jLQw2NYl1gvNlJqhoAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3baaf09b849df9be7ed6bb365562e83b6780c6252c4b6dd0dd9c10964e828f38","last_reissued_at":"2026-07-05T10:06:01.393595Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:01.393595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Daniel Castro, Nuno Santos, Paulo Carreira, Rodrigo Pedro","submitted_at":"2023-08-03T19:03:18Z","abstract_excerpt":"Large Language Models (LLMs) have found widespread applications in various domains, including web applications, where they facilitate human interaction via chatbots with natural language interfaces. Internally, aided by an LLM-integration middleware such as Langchain, user prompts are translated into SQL queries used by the LLM to provide meaningful responses to users. However, unsanitized user prompts can lead to SQL injection attacks, potentially compromising the security of the database. Despite the growing interest in prompt injection vulnerabilities targeting LLMs, the specific risks of g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01990","kind":"arxiv","version":4},"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/2308.01990/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":"2308.01990","created_at":"2026-07-05T10:06:01.393650+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.01990v4","created_at":"2026-07-05T10:06:01.393650+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01990","created_at":"2026-07-05T10:06:01.393650+00:00"},{"alias_kind":"pith_short_12","alias_value":"HOVPBG4ETX43","created_at":"2026-07-05T10:06:01.393650+00:00"},{"alias_kind":"pith_short_16","alias_value":"HOVPBG4ETX4347WW","created_at":"2026-07-05T10:06:01.393650+00:00"},{"alias_kind":"pith_short_8","alias_value":"HOVPBG4E","created_at":"2026-07-05T10:06:01.393650+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26627","citing_title":"Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents","ref_index":91,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28893","citing_title":"Towards Demystifying and Repairing LLM-in-the-Loop Vulnerabilities","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2510.23883","citing_title":"Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges","ref_index":82,"is_internal_anchor":false},{"citing_arxiv_id":"2503.18666","citing_title":"AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01970","citing_title":"Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HOVPBG4ETX4347WWXM3FKYXIHN","json":"https://pith.science/pith/HOVPBG4ETX4347WWXM3FKYXIHN.json","graph_json":"https://pith.science/api/pith-number/HOVPBG4ETX4347WWXM3FKYXIHN/graph.json","events_json":"https://pith.science/api/pith-number/HOVPBG4ETX4347WWXM3FKYXIHN/events.json","paper":"https://pith.science/paper/HOVPBG4E"},"agent_actions":{"view_html":"https://pith.science/pith/HOVPBG4ETX4347WWXM3FKYXIHN","download_json":"https://pith.science/pith/HOVPBG4ETX4347WWXM3FKYXIHN.json","view_paper":"https://pith.science/paper/HOVPBG4E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.01990&json=true","fetch_graph":"https://pith.science/api/pith-number/HOVPBG4ETX4347WWXM3FKYXIHN/graph.json","fetch_events":"https://pith.science/api/pith-number/HOVPBG4ETX4347WWXM3FKYXIHN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HOVPBG4ETX4347WWXM3FKYXIHN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HOVPBG4ETX4347WWXM3FKYXIHN/action/storage_attestation","attest_author":"https://pith.science/pith/HOVPBG4ETX4347WWXM3FKYXIHN/action/author_attestation","sign_citation":"https://pith.science/pith/HOVPBG4ETX4347WWXM3FKYXIHN/action/citation_signature","submit_replication":"https://pith.science/pith/HOVPBG4ETX4347WWXM3FKYXIHN/action/replication_record"}},"created_at":"2026-07-05T10:06:01.393650+00:00","updated_at":"2026-07-05T10:06:01.393650+00:00"}