[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fQ-cHoDkMYP2iMknJ695O0KhrrOzDOtas0ecx1SZyHkQ":3},{"article":4,"iocs":46,"watch_terms":47,"solutions":48,"lesson":137},{"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":23,"category":24,"article_tags":28},"b3e6b186-2f34-4566-86a8-0724be34a0ab","Meta Shares Top Muse Bug Bounty Findings as Payouts Reach $1.2 Million","meta-shares-top-muse-bug-bounty-findings-as-payouts-reach-1-2-million-107c99","A $300,000 award went to a pre-launch flaw that exposed conversations and tokens on internal Muse test VMs used by Meta staff. The post Meta Shares Top Muse Bug Bounty Findings as Payouts Reach $1.2 Million appeared first on SecurityWeek.","Meta has awarded approximately $1.2 million in bug bounties for its Muse AI agent, with a significant $300,000 bounty for a pre-launch vulnerability. This flaw, discovered before Muse's public launch, exposed conversations and tokens on internal test virtual machines. The company also detailed other notable bug payouts, including rewards for issues related to malicious web page summarization, one-time passcode bypass, and an Android app vulnerability.","Meta paid $1.2M in bug bounties for its Muse AI agent, including $300K for a pre-launch data exposure flaw.","https:\u002F\u002Fwww.securityweek.com\u002Fmeta-shares-top-muse-bug-bounty-findings-as-payouts-reach-1-2-million\u002F","https:\u002F\u002Fwww.securityweek.com\u002Fwp-content\u002Fuploads\u002F2026\u002F11\u002FMeta-Muse.jpeg","2026-11-04T08:27:05+00:00","2026-11-04T09:00:05.231079+00:00",7,[17,20],{"name":18,"type":19},"Muse","product",{"name":21,"type":22},"Meta","vendor","e7b231c8-5f79-4465-8d38-1ef13aea5a14",{"id":23,"icon":25,"name":26,"slug":27},null,"Threat Intelligence","threat-intelligence",[29,34,39,44],{"category":30},{"id":31,"icon":25,"name":32,"slug":33},"02371804-cf6d-4449-98de-f1a2d4d9b266","Tools","tools",{"category":35},{"id":36,"icon":25,"name":37,"slug":38},"80544778-fabb-4dcd-aa35-17492e5dcf4f","Vulnerabilities","vulnerabilities",{"category":40},{"id":41,"icon":25,"name":42,"slug":43},"839da5c1-3c34-47e2-9499-f7201640e3ac","AI Security","ai-security",{"category":45},{"id":23,"icon":25,"name":26,"slug":27},[],[21,18],{"controls":49,"vendors":136},[50,79,108],{"slug":51,"name":52,"problem":53,"products":54},"vulnerability-management","Vulnerability scanning and prioritisation","You know which systems are vulnerable, and which ones to fix first.",[55,63,71],{"vendor_slug":56,"vendor_name":57,"vendor_logo":58,"product_slug":59,"product_name":60,"url":61,"description":62},"rapid7","Rapid7","\u002Fvendor-logos\u002Frapid7.png","insightvm","Rapid7 Exposure Command (InsightVM)","https:\u002F\u002Fwww.rapid7.com\u002Fproducts\u002Finsightvm\u002F","Vulnerability scanning and risk-based prioritisation; InsightVM is now part of Exposure Command, alongside attack surface and cloud risk.",{"vendor_slug":64,"vendor_name":65,"vendor_logo":66,"product_slug":67,"product_name":68,"url":69,"description":70},"holm-security","Holm Security","\u002Fvendor-logos\u002Fholm-security.png","holm-security-platform","Holm Security Platform","https:\u002F\u002Fwww.holmsecurity.com\u002F","Vulnerability scanning of systems, web applications, cloud and the external attack surface, plus phishing simulation.",{"vendor_slug":72,"vendor_name":73,"vendor_logo":74,"product_slug":75,"product_name":76,"url":77,"description":78},"tenable","Tenable","\u002Fvendor-logos\u002Ftenable.png","tenable-vulnerability-management","Tenable Vulnerability Management","https:\u002F\u002Fwww.tenable.com\u002Fproducts\u002Fvulnerability-management","Cloud-based vulnerability scanning and prioritisation, built on Nessus.",{"slug":80,"name":81,"problem":82,"products":83},"penetration-testing","Penetration testing and bug bounty","Someone friendly finds the holes before someone hostile does.",[84,92,100],{"vendor_slug":85,"vendor_name":86,"vendor_logo":87,"product_slug":88,"product_name":89,"url":90,"description":91},"hackerone","HackerOne","\u002Fvendor-logos\u002Fhackerone.png","bug-bounty","HackerOne Bug Bounty","https:\u002F\u002Fwww.hackerone.com\u002Fproduct\u002Fbug-bounty-platform","Managed bug bounty and vulnerability disclosure programmes.",{"vendor_slug":93,"vendor_name":94,"vendor_logo":95,"product_slug":96,"product_name":97,"url":98,"description":99},"horizon3-ai","Horizon3.ai","\u002Fvendor-logos\u002Fhorizon3-ai.png","nodezero","NodeZero","https:\u002F\u002Fhorizon3.ai\u002Fnodezero\u002F","Continuous autonomous penetration testing of internal and external networks, cloud, Kubernetes and web applications, showing which attack paths work and checking that fixes hold.",{"vendor_slug":101,"vendor_name":102,"vendor_logo":103,"product_slug":104,"product_name":105,"url":106,"description":107},"safebreach","SafeBreach","\u002Fvendor-logos\u002Fsafebreach.png","safebreach-propagate","SafeBreach Propagate","https:\u002F\u002Fwww.safebreach.com\u002Fpropagate-attack-path-validation\u002F","Automated penetration testing that emulates lateral movement, privilege escalation and credential harvesting to show how far a breach could spread.",{"slug":43,"name":109,"problem":110,"products":111},"AI application security","Prompt injection, data leaks through AI tools and unapproved AI use get caught.",[112,120,128],{"vendor_slug":113,"vendor_name":114,"vendor_logo":115,"product_slug":116,"product_name":117,"url":118,"description":119},"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.",{"vendor_slug":121,"vendor_name":122,"vendor_logo":123,"product_slug":124,"product_name":125,"url":126,"description":127},"cisco","Cisco","\u002Fvendor-logos\u002Fcisco.png","ai-defense","Cisco AI Defense","https:\u002F\u002Fwww.cisco.com\u002Fsite\u002Fus\u002Fen\u002Fproducts\u002Fsecurity\u002Fai-defense\u002Findex.html","Discovery of AI use, testing of models and guardrails for AI applications.",{"vendor_slug":129,"vendor_name":130,"vendor_logo":131,"product_slug":132,"product_name":133,"url":134,"description":135},"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.",[],{"slug":138,"title":139,"body":140,"framework_refs":141,"framework_notes":149},"metas-12m-bug-bounty-highlights-pre-launch-ai-security-gaps","Meta's $1.2M Bug Bounty Highlights Pre-Launch AI Security Gaps","A critical pre-launch vulnerability in Meta's Muse AI agent exposed internal conversations and authentication tokens on test virtual machines, earning a $300,000 bounty — one of the largest in the program's history. This incident illustrates that AI systems, even before public release, can carry severe security flaws that expose sensitive data and credentials if not rigorously tested. The additional findings around OTP bypass, malicious summarization, and mobile app vulnerabilities reveal systemic gaps across the product's attack surface. Bug bounty programs are valuable, but they should complement — not substitute — structured pre-release security testing and configuration hardening. Organizations building AI agents must treat internal test environments with the same security rigor as production systems.",[142,143,144,145,146,147,148],"CIS Control 7: Continuous Vulnerability Management","NIST SP 800-53 SI-2: Flaw Remediation","NIST CSF 2.0 PR.AA: Identity Management, Authentication, and Access Control","ISO\u002FIEC 27001:2022 A.8.8: Management of technical vulnerabilities","OWASP A06:2021 Vulnerable and Outdated Components","GDPR Article 32","DORA Article 9",{"DORA Article 9":150,"GDPR Article 32":151,"NIST SP 800-53 SI-2: Flaw Remediation":152,"OWASP A06:2021 Vulnerable and Outdated Components":153,"CIS Control 7: Continuous Vulnerability Management":154,"ISO\u002FIEC 27001:2022 A.8.8: Management of technical vulnerabilities":155,"NIST CSF 2.0 PR.AA: Identity Management, Authentication, and Access Control":156},"DORA Article 9 mandates resilient and secure ICT systems including thorough pre-deployment testing, directly applicable to ensuring AI agent infrastructure like Muse is hardened before any form of operational use.","The exposure of internal conversations on test VMs may constitute personal data at risk; GDPR Article 32 requires appropriate technical measures to ensure data security, including during development and testing phases.","SI-2 requires organizations to identify and remediate software flaws in a structured, timely manner — a process that should have caught the Muse token-exposure flaw before it reached even internal test infrastructure.","The Android app vulnerability and AI agent flaws reflect the broader OWASP risk of insecure or insufficiently reviewed components being integrated into production-bound software without adequate vetting.","The pre-launch vulnerability exposing tokens and conversations on test VMs would have been detected earlier if continuous vulnerability scanning had been applied to internal environments throughout the development lifecycle.","A.8.8 requires organizations to systematically identify and address technical vulnerabilities in their systems, which applied to Muse's pre-launch phase would have mandated structured vulnerability assessments before internal testing began.","The OTP bypass and token exposure findings both stem from failures in authentication controls; PR.AA mandates robust identity and access management practices that would have constrained these attack vectors."]