{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CX2652DJJCN5VJCMZ44XHZAVUS","short_pith_number":"pith:CX2652DJ","schema_version":"1.0","canonical_sha256":"15f5eee869489bdaa44ccf3973e415a4ba73aa3bddfb094f00aee35ab2f39635","source":{"kind":"arxiv","id":"2601.12983","version":3},"attestation_state":"computed","paper":{"title":"ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Iryna Gurevych, Jesus-German Ortiz-Barajas, Jonathan Tonglet, Vivek Gupta","submitted_at":"2026-01-19T11:57:48Z","abstract_excerpt":"Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, improving analysis and reporting efficiency while introducing new misuse risks. We present ChartAttack, a framework for evaluating how MLLMs can generate misleading charts at scale by injecting misleaders into chart designs to induce incorrect interpretations. We also introduce AttackViz, a chart question-answering (QA) dataset where each (chart specification, QA) pair is labeled with effective misleaders and their induced incorrect answers. ChartAttack significantly degrades QA perfor"},"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":"2601.12983","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-19T11:57:48Z","cross_cats_sorted":[],"title_canon_sha256":"7f03f72286b4645dc23354f492ce53b642d1d12e8831ea28c1491bc5a3162aef","abstract_canon_sha256":"373a7f21661231219d7554e96af4ebd04ad7d3ea6e71d1cb48773f873c0602f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:19.020378Z","signature_b64":"Z+70a4r9+6/rmVy0CUJdyjd8owU4U0o/AQiafBueKgwgKU6EpQWZWpx2QhAleRGvT97K8ZboCr7Znz/ZjslCCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15f5eee869489bdaa44ccf3973e415a4ba73aa3bddfb094f00aee35ab2f39635","last_reissued_at":"2026-06-05T01:15:19.019830Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:19.019830Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Iryna Gurevych, Jesus-German Ortiz-Barajas, Jonathan Tonglet, Vivek Gupta","submitted_at":"2026-01-19T11:57:48Z","abstract_excerpt":"Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, improving analysis and reporting efficiency while introducing new misuse risks. We present ChartAttack, a framework for evaluating how MLLMs can generate misleading charts at scale by injecting misleaders into chart designs to induce incorrect interpretations. We also introduce AttackViz, a chart question-answering (QA) dataset where each (chart specification, QA) pair is labeled with effective misleaders and their induced incorrect answers. ChartAttack significantly degrades QA perfor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.12983","kind":"arxiv","version":3},"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/2601.12983/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":"2601.12983","created_at":"2026-06-05T01:15:19.019900+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.12983v3","created_at":"2026-06-05T01:15:19.019900+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.12983","created_at":"2026-06-05T01:15:19.019900+00:00"},{"alias_kind":"pith_short_12","alias_value":"CX2652DJJCN5","created_at":"2026-06-05T01:15:19.019900+00:00"},{"alias_kind":"pith_short_16","alias_value":"CX2652DJJCN5VJCM","created_at":"2026-06-05T01:15:19.019900+00:00"},{"alias_kind":"pith_short_8","alias_value":"CX2652DJ","created_at":"2026-06-05T01:15:19.019900+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/CX2652DJJCN5VJCMZ44XHZAVUS","json":"https://pith.science/pith/CX2652DJJCN5VJCMZ44XHZAVUS.json","graph_json":"https://pith.science/api/pith-number/CX2652DJJCN5VJCMZ44XHZAVUS/graph.json","events_json":"https://pith.science/api/pith-number/CX2652DJJCN5VJCMZ44XHZAVUS/events.json","paper":"https://pith.science/paper/CX2652DJ"},"agent_actions":{"view_html":"https://pith.science/pith/CX2652DJJCN5VJCMZ44XHZAVUS","download_json":"https://pith.science/pith/CX2652DJJCN5VJCMZ44XHZAVUS.json","view_paper":"https://pith.science/paper/CX2652DJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.12983&json=true","fetch_graph":"https://pith.science/api/pith-number/CX2652DJJCN5VJCMZ44XHZAVUS/graph.json","fetch_events":"https://pith.science/api/pith-number/CX2652DJJCN5VJCMZ44XHZAVUS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CX2652DJJCN5VJCMZ44XHZAVUS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CX2652DJJCN5VJCMZ44XHZAVUS/action/storage_attestation","attest_author":"https://pith.science/pith/CX2652DJJCN5VJCMZ44XHZAVUS/action/author_attestation","sign_citation":"https://pith.science/pith/CX2652DJJCN5VJCMZ44XHZAVUS/action/citation_signature","submit_replication":"https://pith.science/pith/CX2652DJJCN5VJCMZ44XHZAVUS/action/replication_record"}},"created_at":"2026-06-05T01:15:19.019900+00:00","updated_at":"2026-06-05T01:15:19.019900+00:00"}