{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FH5PPFUC6CAB3BQHSQXH5N45ST","short_pith_number":"pith:FH5PPFUC","schema_version":"1.0","canonical_sha256":"29faf79682f0801d8607942e7eb79d94ca1c76c87289dbeb1a076756a185a9c8","source":{"kind":"arxiv","id":"2603.22928","version":2},"attestation_state":"computed","paper":{"title":"SoK: The Attack Surface of Agentic AI - Tools and Autonomy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Ali Dehghantanha, Sajad Homayoun","submitted_at":"2026-03-24T08:21:51Z","abstract_excerpt":"Recent AI systems combine large language models with tools, external knowledge via retrieval-augmented generation (RAG), and even autonomous multi-agent decision loops. This agentic AI paradigm greatly expands capabilities - but also vastly enlarges the attack surface. In this systematization, we map out the trust boundaries and security risks of agentic LLM-based systems. We develop a comprehensive taxonomy of attacks spanning prompt-level injections, knowledge-base poisoning, tool/plug-in exploits, and multi-agent emergent threats. Through a detailed literature review, we synthesize evidence"},"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":"2603.22928","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-03-24T08:21:51Z","cross_cats_sorted":[],"title_canon_sha256":"5585a69c3a3d17e8b7f545f8ed10462e4668af84874e2703ac694935872a6c83","abstract_canon_sha256":"4608fa622a98143dfeb6b514f8ec55a61a725aba063b7cf0569fd91a4e8f0b39"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-12T01:21:40.817196Z","signature_b64":"gwQyf/16TSbJq+iYo6ZImm85MwQjEx8LYMCly1ofDpbbQM+df3h7CoLdPeLFT6TJUQ88yZGQBe30ZbvA9yfGCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29faf79682f0801d8607942e7eb79d94ca1c76c87289dbeb1a076756a185a9c8","last_reissued_at":"2026-08-12T01:21:40.815302Z","signature_status":"signed_v1","first_computed_at":"2026-08-12T01:21:40.815302Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SoK: The Attack Surface of Agentic AI - Tools and Autonomy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Ali Dehghantanha, Sajad Homayoun","submitted_at":"2026-03-24T08:21:51Z","abstract_excerpt":"Recent AI systems combine large language models with tools, external knowledge via retrieval-augmented generation (RAG), and even autonomous multi-agent decision loops. This agentic AI paradigm greatly expands capabilities - but also vastly enlarges the attack surface. In this systematization, we map out the trust boundaries and security risks of agentic LLM-based systems. We develop a comprehensive taxonomy of attacks spanning prompt-level injections, knowledge-base poisoning, tool/plug-in exploits, and multi-agent emergent threats. Through a detailed literature review, we synthesize evidence"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.22928","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/2603.22928/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":"2603.22928","created_at":"2026-08-12T01:21:40.819070+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.22928v2","created_at":"2026-08-12T01:21:40.819070+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.22928","created_at":"2026-08-12T01:21:40.819070+00:00"},{"alias_kind":"pith_short_12","alias_value":"FH5PPFUC6CAB","created_at":"2026-08-12T01:21:40.819070+00:00"},{"alias_kind":"pith_short_16","alias_value":"FH5PPFUC6CAB3BQH","created_at":"2026-08-12T01:21:40.819070+00:00"},{"alias_kind":"pith_short_8","alias_value":"FH5PPFUC","created_at":"2026-08-12T01:21:40.819070+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":10,"sample":[{"citing_arxiv_id":"2607.05743","citing_title":"The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2606.26479","citing_title":"Adaptive Evaluation of Out-of-Band Defenses Against Prompt Injection in LLM Agents","ref_index":32,"is_internal_anchor":true},{"citing_arxiv_id":"2606.23075","citing_title":"Safety in Self-Evolving LLM Agent Systems: Threats, Amplification, and Case Studies","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2606.12320","citing_title":"A Five-Plane Reference Architecture for Runtime Governance of Production AI Agents","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"2606.02668","citing_title":"What You Approve Is What Executes: Consent Integrity for Black-Box LLM Agents","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2606.31639","citing_title":"A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2605.17453","citing_title":"Trust No Tool: Evaluating and Defending LLM Agents under Untrusted Tool Feedback","ref_index":41,"is_internal_anchor":true},{"citing_arxiv_id":"2604.02767","citing_title":"SentinelAgent: Intent-Verified Delegation Chains for Securing Federal Multi-Agent AI Systems","ref_index":28,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10779","citing_title":"LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2604.05969","citing_title":"A Formal Security Framework for MCP-Based AI Agents: Threat Taxonomy, Verification Models, and Defense Mechanisms","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FH5PPFUC6CAB3BQHSQXH5N45ST","json":"https://pith.science/pith/FH5PPFUC6CAB3BQHSQXH5N45ST.json","graph_json":"https://pith.science/api/pith-number/FH5PPFUC6CAB3BQHSQXH5N45ST/graph.json","events_json":"https://pith.science/api/pith-number/FH5PPFUC6CAB3BQHSQXH5N45ST/events.json","paper":"https://pith.science/paper/FH5PPFUC"},"agent_actions":{"view_html":"https://pith.science/pith/FH5PPFUC6CAB3BQHSQXH5N45ST","download_json":"https://pith.science/pith/FH5PPFUC6CAB3BQHSQXH5N45ST.json","view_paper":"https://pith.science/paper/FH5PPFUC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.22928&json=true","fetch_graph":"https://pith.science/api/pith-number/FH5PPFUC6CAB3BQHSQXH5N45ST/graph.json","fetch_events":"https://pith.science/api/pith-number/FH5PPFUC6CAB3BQHSQXH5N45ST/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FH5PPFUC6CAB3BQHSQXH5N45ST/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FH5PPFUC6CAB3BQHSQXH5N45ST/action/storage_attestation","attest_author":"https://pith.science/pith/FH5PPFUC6CAB3BQHSQXH5N45ST/action/author_attestation","sign_citation":"https://pith.science/pith/FH5PPFUC6CAB3BQHSQXH5N45ST/action/citation_signature","submit_replication":"https://pith.science/pith/FH5PPFUC6CAB3BQHSQXH5N45ST/action/replication_record"}},"created_at":"2026-08-12T01:21:40.819070+00:00","updated_at":"2026-08-12T01:21:40.819070+00:00"}