{"schema_version":"0.9.0","id":"LR-2026-0254","record_version":2,"title":"AI-assisted ARTEX hacking campaign targeting financial and personal data across 19 markets","verification":{"human_verified":false,"method":"automated_gate","auto_verified":true,"score":51.7,"score_breakdown":{"source_authority":12.1,"independent_corroboration":7.0,"implicated_party_confirmed":0.0,"citation_integrity":14.0,"specificity":3.0,"completeness":8.0,"unhedged_language":5.0},"checks":{"schema_valid":true,"every_field_cited":true,"quotes_verbatim":true,"source_fetched":true,"distinct_incident":true,"dates_plausible":true,"sources_agree":true,"source_describes_incident":true,"severe_backed_by_source":true,"record_integrity":true},"gate_version":"gate-2026-10-18.7","cross_check_model":null,"cross_check_agreement":null,"duplicate_of":null,"verified_by":null,"verified_at":"2026-10-28","extraction_run_id":null},"system":{"model_name":"DeepSeek, GLM, Grok","model_version":null,"developer":"unknown","harness":"customized ARTEX framework with Claude Code and Codex coding environments","tools_granted":["network_scanning","vulnerability_testing","android_application_analysis","api_discovery","source_code_review"],"permissions_scope":null,"autonomy_level":"full-auto","guardrails_present":[],"deployment_context":"unknown","tier":null,"lifecycle_stage":null,"integration_type":"agentic_with_tools","intended_use":"AI-assisted penetration testing and security testing automation","safeguard_state":null,"production_intended":null},"event":{"date_occurred":null,"date_disclosed":"2026-10-26","incident_class":"security_compromise","failure_class":null,"agent_involved":true,"secondary_failure_classes":[],"trigger":"human_misuse","duration":null,"detection_method":"third_party","summary":"Zscaler's ThreatLabz reported an AI-assisted hacking campaign in which an operator customized the open-source ARTEX penetration-testing framework and used AI coding environments (Claude Code, Codex) with models from DeepSeek, GLM and Grok to scan networks, test vulnerabilities and analyze Android apps.","tags":["ai-assisted-hacking","penetration-testing-framework","multi-model-orchestration","financial-data-targeting","data-theft-marketplaces"],"date_detected":null,"near_miss":null,"root_cause":null,"root_cause_detail":null,"failure_class_detail":null,"responsible_party":null,"adversarial_actor":"external_attacker","input_provenance":null,"time_to_contain":null,"operations_paused":null,"remediation":null,"precursor_events":null},"impact":{"direct_loss_usd":null,"remediation_hours":null,"records_exposed":null,"third_parties_affected_count":null,"severity":"major","severity_because":"major: Prompts referenced organizations in 19 countries and territories, an unbounded multi-country scope, though compromise was not established.","harm_class":[],"affected_description":"organizations in 19 countries and territories across financial services, cryptocurrency, retail, education, logistics, and other industries","protected_class_involved":null,"data_categories_exposed":[],"loss_range_usd":null,"reversibility":null,"downstream_exposure_count":null},"evidence":{"sources":[{"url":"https://www.crnasia.com/news/2026/cybersecurity/ai-assisted-hackers-target-financial-and-personal-data-across-19-markets","publisher":"news search (Google News index)","date":"2026-10-26","type":"press","title":"Zscaler: AI-assisted hackers target financial and personal data across 19 markets","archived_url":null,"attachment":"first_report"}],"per_field_confidence":{"system.model_name":"C","system.harness":"C","system.tools_granted":"C","system.integration_type":"C","system.intended_use":"C","system.developer":"D","system.autonomy_level":"C","system.deployment_context":"D","event.tags":"D","event.adversarial_actor":"C","event.agent_involved":"C","event.trigger":"C","event.detection_method":"C","event.summary":"D","event.incident_class":"C","event.date_disclosed":"A","impact.affected_description":"C","impact.severity":"D","context.industry":"D","context.systemic_exposure":"D","context.denominator_available":"D","context.country":"D","impact.severity_because":"D"},"field_citations":{"system.model_name":[{"source_index":0,"quote":"The operator used AI coding environments, including Claude Code and Codex, alongside configurations specifying models from DeepSeek, GLM, and Grok."}],"system.developer":[{"source_index":0,"quote":"Zscaler noted, however, that it could not independently verify which AI models processed every session."}],"system.harness":[{"source_index":0,"quote":"In its recent report, Zscaler's ThreatLabz researchers found that the attacker used a customized version of ARTEX, an AI-assisted penetration testing framework, alongside coding assistants and automation scripts."}],"system.tools_granted":[{"source_index":0,"quote":"According to Zscaler, the attacker modified the open-source ARTEX framework to support network scanning, vulnerability testing, and Android application analysis."},{"source_index":0,"quote":"Additional instructions assigned specialized AI agents to discover APIs, review source code, and examine Android applications."}],"system.autonomy_level":[{"source_index":0,"quote":"Some configurations permitted actions without individual operator approval, while prompts instructed the tools to treat assigned targets as already authorized for testing."}],"system.integration_type":[{"source_index":0,"quote":"The framework uses AI agents to break down security testing objectives into individual tasks and execute them using available tools."}],"system.intended_use":[{"source_index":0,"quote":"According to Zscaler, the attacker modified the open-source ARTEX framework to support network scanning, vulnerability testing, and Android application analysis."}],"event.trigger":[{"source_index":0,"quote":"In its recent report, Zscaler's ThreatLabz researchers found that the attacker used a customized version of ARTEX, an AI-assisted penetration testing framework, alongside coding assistants and automation scripts."}],"event.detection_method":[{"source_index":0,"quote":"In its recent report, Zscaler's ThreatLabz researchers found that the attacker used a customized version of ARTEX, an AI-assisted penetration testing framework, alongside coding assistants and automation scripts."}],"event.summary":[{"source_index":0,"quote":"In its recent report, Zscaler's ThreatLabz researchers found that the attacker used a customized version of ARTEX, an AI-assisted penetration testing framework, alongside coding assistants and automation scripts."}],"event.tags":[{"source_index":0,"quote":"Other prompts sought information about marketplaces for selling stolen data."}],"event.adversarial_actor":[{"source_index":0,"quote":"In its recent report, Zscaler's ThreatLabz researchers found that the attacker used a customized version of ARTEX, an AI-assisted penetration testing framework, alongside coding assistants and automation scripts."}],"event.incident_class":[{"source_index":0,"quote":"In its recent report, Zscaler's ThreatLabz researchers found that the attacker used a customized version of ARTEX, an AI-assisted penetration testing framework, alongside coding assistants and automation scripts."}],"event.agent_involved":[{"source_index":0,"quote":"The framework uses AI agents to break down security testing objectives into individual tasks and execute them using available tools."}],"impact.affected_description":[{"source_index":0,"quote":"The records covered organizations across financial services, cryptocurrency, retail, education, logistics, and other industries."}],"impact.severity_because":[{"source_index":0,"quote":"Zscaler identified prompts referencing organizations in 19 countries and territories, including Malaysia, Indonesia, Thailand, India, Japan, South Korea, the United Kingdom, and the United States."}],"context.industry":[{"source_index":0,"quote":"The records covered organizations across financial services, cryptocurrency, retail, education, logistics, and other industries."}],"context.country":[{"source_index":0,"quote":"Zscaler identified prompts referencing organizations in 19 countries and territories, including Malaysia, Indonesia, Thailand, India, Japan, South Korea, the United Kingdom, and the United States."}],"context.systemic_exposure":[{"source_index":0,"quote":"Zscaler noted, however, that it could not independently verify which AI models processed every session."}],"impact.severity":[{"source_index":0,"quote":"Zscaler identified prompts referencing organizations in 19 countries and territories, including Malaysia, Indonesia, Thailand, India, Japan, South Korea, the United Kingdom, and the United States."}],"event.date_disclosed":[{"source_index":0,"quote":"published: 2026-10-26"}]},"notes_on_gaps":"No source states a date the failure occurred, a disclosure date, a loss figure, record counts, third-party affected counts, containment, notification or remediation, so those fields are omitted. event.failure_class is left null: the sources describe an attacker using AI tooling, not an agent-mechanism failure, and no failure_class test is met. event.root_cause omitted: no source names what failed. system.deployment_context omitted: no source states where the system ran. system.permissions_scope, safeguard_state, production_intended, lifecycle_stage omitted as unstated. impact.harm_class left empty: no source states harm materialised, since compromise was not established. impact.third_parties_affected_count left null, not zero. context.org_size_band, denominator, regulatory, coverage, incident_evidence omitted as unstated. Judgements: system.developer set to unknown because Zscaler could not verify which models processed each session; autonomy full-auto rests on configurations permitting actions without operator approval; detection_method third_party rests on Zscaler's researchers finding the activity; incident_class security_compromise rests on an attacker driving the system; trigger human_misuse rests on the attacker deliberately using the framework; severity major rests on the unbounded 19-country scope, though no compromise was established. system.tier: not set; no operator named and deployment_context is not a lab's. [aggregated by deepseek-chat, aggregate-2026-09-27.1, 2026-10-28] system.developer: no source names the developer. system.deployment_context: no source states where it ran. event.tags: classified from described behaviour. event.summary: classified from described behaviour. context.industry: classified from described behaviour. context.systemic_exposure: classified from described behaviour. context.denominator_available: no denominator is available for this incident. context.country: no source states the operator's location."},"context":{"industry":"Finance and Insurance","org_size_band":null,"country":"unknown","denominator_available":false,"denominator":null,"related_incident_ids":[],"systemic_exposure":false,"shared_model_id":null,"upstream_incident_id":null,"incident_evidence":null,"regulatory":null,"coverage":null},"narrative":{"steps":[{"step":"doing","text":"An operator ran a customized ARTEX penetration-testing framework with Claude Code and Codex and models from DeepSeek, GLM and Grok, using agents to scan networks, test vulnerabilities, review code and analyze Android apps.","quote":"According to Zscaler, the attacker modified the open-source ARTEX framework to support network scanning, vulnerability testing, and Android application analysis.","source_index":0,"grade":"C"},{"step":"went_wrong","text":"The AI tools were used to pursue unauthorized intrusion against real organizations, with prompts treating targets as already authorized and some configurations permitting actions without operator approval.","quote":"Some configurations permitted actions without individual operator approval, while prompts instructed the tools to treat assigned targets as already authorized for testing.","source_index":0,"grade":"C"},{"step":"reached","text":"Prompts referenced organizations in 19 countries across financial services, cryptocurrency, retail, education and logistics, seeking personal information, financial records and database contents; compromise was not established.","quote":"Zscaler identified prompts referencing organizations in 19 countries and territories, including Malaysia, Indonesia, Thailand, India, Japan, South Korea, the United Kingdom, and the United States.","source_index":0,"grade":"C"},{"step":"caught","text":"Zscaler's ThreatLabz researchers found the activity by examining configuration files, scripts and AI prompt histories recorded between August 4 and October 4.","quote":"The investigation examined configuration files, scripts, and AI prompt histories recorded between August 4 and October 4, which showed attempts to obtain personal information, financial records, and other sensitive data.","source_index":0,"grade":"C"}],"model":"deepseek-chat","prompt_version":"aggregate-2026-09-27.1","extracted_at":"2026-10-28","source_urls":["https://www.crnasia.com/news/2026/cybersecurity/ai-assisted-hackers-target-financial-and-personal-data-across-19-markets"]}}