{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GR5YU5F4OWQVFOWYNPPBDZVHPH","short_pith_number":"pith:GR5YU5F4","schema_version":"1.0","canonical_sha256":"347b8a74bc75a152bad86bde11e6a779ca65246da6d84964bd6866e0dc34fc43","source":{"kind":"arxiv","id":"2502.13175","version":2},"attestation_state":"computed","paper":{"title":"Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CR","authors_text":"Meng Han, Minghao Li, Mohan Li, Wenpeng Xing","submitted_at":"2025-02-18T03:38:07Z","abstract_excerpt":"Embodied AI systems, including robots and autonomous vehicles, are increasingly integrated into real-world applications, where they encounter a range of vulnerabilities stemming from both environmental and system-level factors. These vulnerabilities manifest through sensor spoofing, adversarial attacks, and failures in task and motion planning, posing significant challenges to robustness and safety. Despite the growing body of research, existing reviews rarely focus specifically on the unique safety and security challenges of embodied AI systems. Most prior work either addresses general AI vul"},"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":"2502.13175","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-02-18T03:38:07Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"701395c668324f5aa6d6674dc289b35efd0ed05db63e735baf21d20c9a20836a","abstract_canon_sha256":"871812a8d10c0a29191069c2d36cb442a1e27668cbc43261e7ea89fd3301b9e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:39.531284Z","signature_b64":"qnxtBXPBlVrnkDO160MXqRIM+LwOEJow+kgDdhP8FgIwdWSSLnOuXUCFJxhiRMKFEL2biEb1z1uFFkqOA437Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"347b8a74bc75a152bad86bde11e6a779ca65246da6d84964bd6866e0dc34fc43","last_reissued_at":"2026-07-05T10:19:39.530801Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:39.530801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CR","authors_text":"Meng Han, Minghao Li, Mohan Li, Wenpeng Xing","submitted_at":"2025-02-18T03:38:07Z","abstract_excerpt":"Embodied AI systems, including robots and autonomous vehicles, are increasingly integrated into real-world applications, where they encounter a range of vulnerabilities stemming from both environmental and system-level factors. These vulnerabilities manifest through sensor spoofing, adversarial attacks, and failures in task and motion planning, posing significant challenges to robustness and safety. Despite the growing body of research, existing reviews rarely focus specifically on the unique safety and security challenges of embodied AI systems. Most prior work either addresses general AI vul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.13175","kind":"arxiv","version":2},"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/2502.13175/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":"2502.13175","created_at":"2026-07-05T10:19:39.530859+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.13175v2","created_at":"2026-07-05T10:19:39.530859+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.13175","created_at":"2026-07-05T10:19:39.530859+00:00"},{"alias_kind":"pith_short_12","alias_value":"GR5YU5F4OWQV","created_at":"2026-07-05T10:19:39.530859+00:00"},{"alias_kind":"pith_short_16","alias_value":"GR5YU5F4OWQVFOWY","created_at":"2026-07-05T10:19:39.530859+00:00"},{"alias_kind":"pith_short_8","alias_value":"GR5YU5F4","created_at":"2026-07-05T10:19:39.530859+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19998","citing_title":"Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25653","citing_title":"When Agents Control Robots: A Zero Trust Policy Model for Agentic Cyber-Physical Systems","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07457","citing_title":"CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07835","citing_title":"Silencing the Guardrails: Inference-Time Jailbreaking via Dynamic Contextual Representation Ablation","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GR5YU5F4OWQVFOWYNPPBDZVHPH","json":"https://pith.science/pith/GR5YU5F4OWQVFOWYNPPBDZVHPH.json","graph_json":"https://pith.science/api/pith-number/GR5YU5F4OWQVFOWYNPPBDZVHPH/graph.json","events_json":"https://pith.science/api/pith-number/GR5YU5F4OWQVFOWYNPPBDZVHPH/events.json","paper":"https://pith.science/paper/GR5YU5F4"},"agent_actions":{"view_html":"https://pith.science/pith/GR5YU5F4OWQVFOWYNPPBDZVHPH","download_json":"https://pith.science/pith/GR5YU5F4OWQVFOWYNPPBDZVHPH.json","view_paper":"https://pith.science/paper/GR5YU5F4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.13175&json=true","fetch_graph":"https://pith.science/api/pith-number/GR5YU5F4OWQVFOWYNPPBDZVHPH/graph.json","fetch_events":"https://pith.science/api/pith-number/GR5YU5F4OWQVFOWYNPPBDZVHPH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GR5YU5F4OWQVFOWYNPPBDZVHPH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GR5YU5F4OWQVFOWYNPPBDZVHPH/action/storage_attestation","attest_author":"https://pith.science/pith/GR5YU5F4OWQVFOWYNPPBDZVHPH/action/author_attestation","sign_citation":"https://pith.science/pith/GR5YU5F4OWQVFOWYNPPBDZVHPH/action/citation_signature","submit_replication":"https://pith.science/pith/GR5YU5F4OWQVFOWYNPPBDZVHPH/action/replication_record"}},"created_at":"2026-07-05T10:19:39.530859+00:00","updated_at":"2026-07-05T10:19:39.530859+00:00"}