{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:INPRQBWWSSL7TT24WE3J3VOMFM","short_pith_number":"pith:INPRQBWW","schema_version":"1.0","canonical_sha256":"435f1806d69497f9cf5cb1369dd5cc2b0177ad2b545e0b78dc7f7c0fbde688a1","source":{"kind":"arxiv","id":"2502.13053","version":3},"attestation_state":"computed","paper":{"title":"Evaluating the Robustness of Multimodal Agents Against Active Environmental Injection Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Juncheng Li, Keting Yin, Shengyu Zhang, Xavier Hu, Yurun Chen","submitted_at":"2025-02-18T17:01:28Z","abstract_excerpt":"As researchers continue to optimize AI agents for more effective task execution within operating systems, they often overlook a critical security concern: the ability of these agents to detect \"impostors\" within their environment. Through an analysis of the agents' operational context, we identify a significant threat-attackers can disguise malicious attacks as environmental elements, injecting active disturbances into the agents' execution processes to manipulate their decision-making. We define this novel threat as the Active Environment Injection Attack (AEIA). Focusing on the interaction m"},"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":"2502.13053","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-18T17:01:28Z","cross_cats_sorted":[],"title_canon_sha256":"85cb4d68d1bf3858bd87a3d1dfc3e0291d37676d6ccb931ffc49e37f8a300da5","abstract_canon_sha256":"df80336ede3f24b937207eefc71fb51f6dc7612f2f365ead900ea06afce5717f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:07.123439Z","signature_b64":"rRp5e7zSHsK0UtR+FOz+3mlV6SR+sOdY9NX4Wd4Af1MSYz4LNiTpk05Q9vJeaEcIUUmAbqlfrKo5w2bY2DocAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"435f1806d69497f9cf5cb1369dd5cc2b0177ad2b545e0b78dc7f7c0fbde688a1","last_reissued_at":"2026-07-05T11:49:07.122934Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:07.122934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating the Robustness of Multimodal Agents Against Active Environmental Injection Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Juncheng Li, Keting Yin, Shengyu Zhang, Xavier Hu, Yurun Chen","submitted_at":"2025-02-18T17:01:28Z","abstract_excerpt":"As researchers continue to optimize AI agents for more effective task execution within operating systems, they often overlook a critical security concern: the ability of these agents to detect \"impostors\" within their environment. Through an analysis of the agents' operational context, we identify a significant threat-attackers can disguise malicious attacks as environmental elements, injecting active disturbances into the agents' execution processes to manipulate their decision-making. We define this novel threat as the Active Environment Injection Attack (AEIA). Focusing on the interaction m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.13053","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/2502.13053/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":"2502.13053","created_at":"2026-07-05T11:49:07.122994+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.13053v3","created_at":"2026-07-05T11:49:07.122994+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.13053","created_at":"2026-07-05T11:49:07.122994+00:00"},{"alias_kind":"pith_short_12","alias_value":"INPRQBWWSSL7","created_at":"2026-07-05T11:49:07.122994+00:00"},{"alias_kind":"pith_short_16","alias_value":"INPRQBWWSSL7TT24","created_at":"2026-07-05T11:49:07.122994+00:00"},{"alias_kind":"pith_short_8","alias_value":"INPRQBWW","created_at":"2026-07-05T11:49:07.122994+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10749","citing_title":"Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2507.10610","citing_title":"LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack on GUI Agents","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2510.23883","citing_title":"Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges","ref_index":161,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09497","citing_title":"Don't Click That: Teaching Web Agents to Resist Deceptive Interfaces","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/INPRQBWWSSL7TT24WE3J3VOMFM","json":"https://pith.science/pith/INPRQBWWSSL7TT24WE3J3VOMFM.json","graph_json":"https://pith.science/api/pith-number/INPRQBWWSSL7TT24WE3J3VOMFM/graph.json","events_json":"https://pith.science/api/pith-number/INPRQBWWSSL7TT24WE3J3VOMFM/events.json","paper":"https://pith.science/paper/INPRQBWW"},"agent_actions":{"view_html":"https://pith.science/pith/INPRQBWWSSL7TT24WE3J3VOMFM","download_json":"https://pith.science/pith/INPRQBWWSSL7TT24WE3J3VOMFM.json","view_paper":"https://pith.science/paper/INPRQBWW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.13053&json=true","fetch_graph":"https://pith.science/api/pith-number/INPRQBWWSSL7TT24WE3J3VOMFM/graph.json","fetch_events":"https://pith.science/api/pith-number/INPRQBWWSSL7TT24WE3J3VOMFM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/INPRQBWWSSL7TT24WE3J3VOMFM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/INPRQBWWSSL7TT24WE3J3VOMFM/action/storage_attestation","attest_author":"https://pith.science/pith/INPRQBWWSSL7TT24WE3J3VOMFM/action/author_attestation","sign_citation":"https://pith.science/pith/INPRQBWWSSL7TT24WE3J3VOMFM/action/citation_signature","submit_replication":"https://pith.science/pith/INPRQBWWSSL7TT24WE3J3VOMFM/action/replication_record"}},"created_at":"2026-07-05T11:49:07.122994+00:00","updated_at":"2026-07-05T11:49:07.122994+00:00"}