{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:B6HLT3VK7AS6ZJHLTVBEXRUWFA","short_pith_number":"pith:B6HLT3VK","schema_version":"1.0","canonical_sha256":"0f8eb9eeaaf825eca4eb9d424bc6962803c4adfd914f7f83762ac77081dc2bee","source":{"kind":"arxiv","id":"2506.16653","version":1},"attestation_state":"computed","paper":{"title":"LLMs in Coding and their Impact on the Commercial Software Engineering Landscape","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.SE","authors_text":"Askhan Sami, Peter J Barclay, Vladislav Belozerov","submitted_at":"2025-06-19T23:43:54Z","abstract_excerpt":"Large-language-model coding tools are now mainstream in software engineering. But as these same tools move human effort up the development stack, they present fresh dangers: 10% of real prompts leak private data, 42% of generated snippets hide security flaws, and the models can even ``agree'' with wrong ideas, a trait called sycophancy. We argue that firms must tag and review every AI-generated line of code, keep prompts and outputs inside private or on-premises deployments, obey emerging safety regulations, and add tests that catch sycophantic answers -- so they can gain speed without losing "},"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":"2506.16653","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-06-19T23:43:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3946419468d854b0cbb49fd7120f7c2d384f7de4c1bfc911df4ef14701d31a9f","abstract_canon_sha256":"a7ecdb5d2f173117f49c62332872e5ced5da39943a544a474176b8e681da2e75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:40.220634Z","signature_b64":"NTzNbPhRn1jVZ7y3ziwdbhupW49EuOQIwpZS2Sc7mn/JSMe/o69yXW+3ljlOo8INE40gmwhetJDtl76TXPUbDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f8eb9eeaaf825eca4eb9d424bc6962803c4adfd914f7f83762ac77081dc2bee","last_reissued_at":"2026-07-05T11:24:40.220139Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:40.220139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLMs in Coding and their Impact on the Commercial Software Engineering Landscape","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.SE","authors_text":"Askhan Sami, Peter J Barclay, Vladislav Belozerov","submitted_at":"2025-06-19T23:43:54Z","abstract_excerpt":"Large-language-model coding tools are now mainstream in software engineering. But as these same tools move human effort up the development stack, they present fresh dangers: 10% of real prompts leak private data, 42% of generated snippets hide security flaws, and the models can even ``agree'' with wrong ideas, a trait called sycophancy. We argue that firms must tag and review every AI-generated line of code, keep prompts and outputs inside private or on-premises deployments, obey emerging safety regulations, and add tests that catch sycophantic answers -- so they can gain speed without losing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.16653","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/2506.16653/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":"2506.16653","created_at":"2026-07-05T11:24:40.220201+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.16653v1","created_at":"2026-07-05T11:24:40.220201+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16653","created_at":"2026-07-05T11:24:40.220201+00:00"},{"alias_kind":"pith_short_12","alias_value":"B6HLT3VK7AS6","created_at":"2026-07-05T11:24:40.220201+00:00"},{"alias_kind":"pith_short_16","alias_value":"B6HLT3VK7AS6ZJHL","created_at":"2026-07-05T11:24:40.220201+00:00"},{"alias_kind":"pith_short_8","alias_value":"B6HLT3VK","created_at":"2026-07-05T11:24:40.220201+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B6HLT3VK7AS6ZJHLTVBEXRUWFA","json":"https://pith.science/pith/B6HLT3VK7AS6ZJHLTVBEXRUWFA.json","graph_json":"https://pith.science/api/pith-number/B6HLT3VK7AS6ZJHLTVBEXRUWFA/graph.json","events_json":"https://pith.science/api/pith-number/B6HLT3VK7AS6ZJHLTVBEXRUWFA/events.json","paper":"https://pith.science/paper/B6HLT3VK"},"agent_actions":{"view_html":"https://pith.science/pith/B6HLT3VK7AS6ZJHLTVBEXRUWFA","download_json":"https://pith.science/pith/B6HLT3VK7AS6ZJHLTVBEXRUWFA.json","view_paper":"https://pith.science/paper/B6HLT3VK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.16653&json=true","fetch_graph":"https://pith.science/api/pith-number/B6HLT3VK7AS6ZJHLTVBEXRUWFA/graph.json","fetch_events":"https://pith.science/api/pith-number/B6HLT3VK7AS6ZJHLTVBEXRUWFA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B6HLT3VK7AS6ZJHLTVBEXRUWFA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B6HLT3VK7AS6ZJHLTVBEXRUWFA/action/storage_attestation","attest_author":"https://pith.science/pith/B6HLT3VK7AS6ZJHLTVBEXRUWFA/action/author_attestation","sign_citation":"https://pith.science/pith/B6HLT3VK7AS6ZJHLTVBEXRUWFA/action/citation_signature","submit_replication":"https://pith.science/pith/B6HLT3VK7AS6ZJHLTVBEXRUWFA/action/replication_record"}},"created_at":"2026-07-05T11:24:40.220201+00:00","updated_at":"2026-07-05T11:24:40.220201+00:00"}