[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fT2hHR1EgRQAd0m9OA_OCVbUrqf0IEnYLBOVs8R7ufoA":3},{"article":4,"iocs":52,"watch_terms":57,"solutions":58,"lesson":147},{"id":5,"title":6,"slug":7,"summary":8,"ai_summary":9,"brief":10,"url":11,"image_url":12,"published_at":13,"ingested_at":14,"relevance_score":15,"entities":16,"category_id":29,"category":30,"article_tags":34},"89eff9af-31cc-4c09-b007-d0c693b47732","OpenAI Disrupts Reasoning Extraction Campaign Linked to Moonshot AI Associates","openai-disrupts-reasoning-extraction-campaign-linked-to-moonshot-ai-associates-33c11d","OpenAI on Wednesday said it identified and disrupted a coordinated distillation campaign that was designed to illicitly extract protected reasoning from its artificial intelligence (AI) models. A \"core cluster of the activity,\" going back to the first week of July, has been attributed to individuals associated with Moonshot AI, a Chinese AI company based in Beijing. It did not cite any","OpenAI has disrupted a coordinated campaign aimed at illicitly extracting protected reasoning from its AI models. The activity, attributed to individuals associated with Chinese AI company Moonshot AI, involved manipulating model interactions to reproduce protected reasoning. OpenAI has since implemented additional mitigations and banned fraudulent accounts.","OpenAI disrupted a campaign to extract AI model reasoning, linked to China-based Moonshot AI.","https:\u002F\u002Fthehackernews.com\u002F2026\u002F10\u002Fopenai-disrupts-reasoning-extraction.html","https:\u002F\u002Fblogger.googleusercontent.com\u002Fimg\u002Fb\u002FR29vZ2xl\u002FAVvXsEgVoOullmVukPDhwbQ10zXHA-IqDVeMp55dybdLAhT-kkPDdJADbw8BETggWjHN3WF4HHpXLV8RDk4jHLVYlS9wWxZcbHMtEGGfGu-dGv2lFRzv3lJ1csgozieAguSQeE2Nhw5ZXBi1YIjaBbF5Al_kakh4wNXfjr4PJ-Q4QZxaHm4_Lha_aMxdQ3NPKRm0\u002Fs1600\u002Fmoonshot.jpg","2026-10-01T10:42:36+00:00","2026-10-01T12:00:16.766999+00:00",8,[17,20,22,24,27],{"name":18,"type":19},"OpenAI","vendor",{"name":21,"type":19},"Moonshot AI",{"name":23,"type":19},"Anthropic",{"name":25,"type":26},"Claude","product",{"name":28,"type":26},"Kimi","839da5c1-3c34-47e2-9499-f7201640e3ac",{"id":29,"icon":31,"name":32,"slug":33},null,"AI Security","ai-security",[35,40,42,47],{"category":36},{"id":37,"icon":31,"name":38,"slug":39},"6cbdd207-aaa1-4176-9534-e156b125e917","Nation-state","nation-state",{"category":41},{"id":29,"icon":31,"name":32,"slug":33},{"category":43},{"id":44,"icon":31,"name":45,"slug":46},"c5c77cdb-f7d7-4990-9436-c81dcbff1163","Policy","policy",{"category":48},{"id":49,"icon":31,"name":50,"slug":51},"e7b231c8-5f79-4465-8d38-1ef13aea5a14","Threat Intelligence","threat-intelligence",[53],{"type":54,"value":55,"context":56},"malware","adversarial distillation","Method used to extract protected reasoning from AI models.",[18,21,23],{"controls":59,"vendors":146},[60,89,118],{"slug":61,"name":62,"problem":63,"products":64},"identity-threat-detection","Identity threat detection and response (ITDR)","Attackers who log in with real, stolen credentials get noticed.",[65,73,81],{"vendor_slug":66,"vendor_name":67,"vendor_logo":68,"product_slug":69,"product_name":70,"url":71,"description":72},"acalvio","Acalvio","\u002Fvendor-logos\u002Facalvio.png","shadowplex-identity-protection","ShadowPlex Identity Protection","https:\u002F\u002Fwww.acalvio.com\u002Fproducts\u002Fidentity-protection\u002F","Decoy accounts and credentials that expose identity attacks and credential misuse early.",{"vendor_slug":74,"vendor_name":75,"vendor_logo":76,"product_slug":77,"product_name":78,"url":79,"description":80},"crowdstrike","CrowdStrike","\u002Fvendor-logos\u002Fcrowdstrike.png","falcon-identity-protection","Falcon Identity Protection","https:\u002F\u002Fwww.crowdstrike.com\u002Fen-us\u002Fplatform\u002Fnext-gen-identity-security\u002Fidentity-protection\u002F","Detection of identity attacks against Active Directory and Entra ID, with risk-based MFA.",{"vendor_slug":82,"vendor_name":83,"vendor_logo":84,"product_slug":85,"product_name":86,"url":87,"description":88},"vectra-ai","Vectra AI","\u002Fvendor-logos\u002Fvectra-ai.png","vectra-ai-platform","Vectra AI Platform","https:\u002F\u002Fwww.vectra.ai\u002Fplatform","Detection of attacker behaviour across network, identity and cloud.",{"slug":90,"name":91,"problem":92,"products":93},"security-monitoring-siem","Security monitoring (SIEM)","Logs from everywhere end up in one place, with alerts that someone actually sees.",[94,102,110],{"vendor_slug":95,"vendor_name":96,"vendor_logo":97,"product_slug":98,"product_name":99,"url":100,"description":101},"elastic","Elastic","\u002Fvendor-logos\u002Felastic.png","elastic-security","Elastic Security","https:\u002F\u002Fwww.elastic.co\u002Fsecurity","SIEM and endpoint security built on Elasticsearch, with open detection rules.",{"vendor_slug":103,"vendor_name":104,"vendor_logo":105,"product_slug":106,"product_name":107,"url":108,"description":109},"splunk","Splunk","\u002Fvendor-logos\u002Fsplunk.png","enterprise-security","Splunk Enterprise Security","https:\u002F\u002Fwww.splunk.com\u002Fen_us\u002Fproducts\u002Fenterprise-security.html","SIEM for detection, investigation and response, on premises or in the cloud.",{"vendor_slug":111,"vendor_name":112,"vendor_logo":113,"product_slug":114,"product_name":115,"url":116,"description":117},"vega","Vega","\u002Fvendor-logos\u002Fvega.png","vega-agentic-detection","Agentic Detection","https:\u002F\u002Fvega.io\u002Fplatform\u002Fagentic-detection","Detection rules that run across security data in the stores where it already lives, so logs do not have to be moved into a central SIEM first.",{"slug":33,"name":119,"problem":120,"products":121},"AI application security","Prompt injection, data leaks through AI tools and unapproved AI use get caught.",[122,130,138],{"vendor_slug":123,"vendor_name":124,"vendor_logo":125,"product_slug":126,"product_name":127,"url":128,"description":129},"boomi","Boomi","\u002Fvendor-logos\u002Fboomi.png","boomi-ai-gateway","Boomi AI Gateway","https:\u002F\u002Fboomi.com\u002Fplatform\u002Fai-gateway\u002F","Gateway for AI model and MCP tool traffic, built on Lunar.dev technology. It limits which tools each agent can call. Its enterprise version adds an approval step for new MCP servers and logs each call with the user or agent behind it.",{"vendor_slug":131,"vendor_name":132,"vendor_logo":133,"product_slug":134,"product_name":135,"url":136,"description":137},"hiddenlayer","HiddenLayer","\u002Fvendor-logos\u002Fhiddenlayer.png","hiddenlayer-ai-security","HiddenLayer AI Security Platform","https:\u002F\u002Fhiddenlayer.com\u002F","Model scanning, AI red teaming and runtime protection for AI applications.",{"vendor_slug":139,"vendor_name":140,"vendor_logo":141,"product_slug":142,"product_name":143,"url":144,"description":145},"palo-alto-networks","Palo Alto Networks","\u002Fvendor-logos\u002Fpalo-alto-networks.png","prisma-airs","Prisma AIRS","https:\u002F\u002Fwww.paloaltonetworks.com\u002Fai-security\u002Fprisma-airs","Security for AI applications, models and agents, including runtime protection against prompt injection.",[],{"slug":148,"title":149,"body":150,"framework_refs":151,"framework_notes":162},"ai-model-reasoning-extraction-thwarted-via-account-manipulation","AI Model Reasoning Extraction Thwarted via Account Manipulation","Individuals linked to Moonshot AI exploited manipulative interaction techniques to illicitly extract protected reasoning outputs from OpenAI's models, effectively attempting to steal proprietary intellectual property through misuse of the platform itself. The root issue lies in insufficient behavioral monitoring and access controls that failed to detect coordinated, systematic abuse before significant extraction occurred. This matters because AI model reasoning and weights represent high-value trade secrets, and adversarial extraction techniques can undermine competitive advantage and safety boundaries built into these systems. The incident highlights that technical misuse of AI APIs is an emerging threat vector that requires dedicated detection strategies beyond traditional cybersecurity controls.",[152,153,154,155,156,157,158,159,160,161],"NIST AI RMF - GOVERN 1.2 (Policies for AI risk)","NIST SP 800-53 AC-2 (Account Management)","NIST SP 800-53 AU-6 (Audit Review and Analysis)","NIST SP 800-53 SI-4 (System Monitoring)","CIS Control 5 (Account Management)","CIS Control 8 (Audit Log Management)","CIS Control 13 (Network Monitoring and Defense)","GDPR Article 25 (Data Protection by Design)","MITRE ATLAS AML.T0016 (Obtain Capabilities via Extraction)","ISO\u002FIEC 42001 (AI Management System Standard)",{}]