[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$frCZ6dkqNUIz_Z5sMBYOt8awhBOFxEaqjwo43V5Hxf58":3},{"article":4,"iocs":48,"watch_terms":49,"solutions":50,"lesson":112},{"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":30,"category":31,"article_tags":35},"a4a5cd2e-1e5b-4e4d-a209-a6555c5dace3","GitHub Agentic Autofix and the question of who owns a bad AI patch","github-agentic-autofix-and-the-question-of-who-owns-a-bad-ai-patch-c706ca","In this Help Net Security interview, Marcelo Oliveira, VP of Product Management at GitHub, talks about who is accountable when an AI-generated patch fixes one flaw and introduces another. He explains how Agentic Autofix re-runs detection engines and project tests, and what a CISO should change before pointing an agent at thousands of old alerts. Oliveira also discusses which metrics show a codebase is getting safer, how GitHub picks the models behind autofix, and whether … More → The post GitHub Agentic Autofix and the question of who owns a bad AI patch appeared first on Help Net Security.","GitHub's Agentic Autofix aims to automatically remediate code vulnerabilities, but raises questions about accountability when AI-generated patches introduce new flaws. The company states they are accountable for suggested fixes, while customers decide what to merge. The tool re-runs detection engines and project tests to ensure fix effectiveness, and has shown a four times higher security backlog fix rate compared to human-only baselines.","GitHub discusses accountability for AI-generated code patches and the implications for security.","https:\u002F\u002Fwww.helpnetsecurity.com\u002F2026\u002F11\u002F12\u002Fmarcelo-oliveira-github-agentic-autofix\u002F","https:\u002F\u002Fimg.helpnetsecurity.com\u002Fwp-content\u002Fuploads\u002F2026\u002F11\u002F10125239\u002Fmarcelo_oliveira-2-github.webp","2026-11-12T07:00:38+00:00","2026-11-12T08:00:34.213538+00:00",7,[17,20,22,24,27],{"name":18,"type":19},"Agentic Autofix","product",{"name":21,"type":19},"Copilot Autofix",{"name":23,"type":19},"CodeQL",{"name":25,"type":26},"GitHub","vendor",{"name":28,"type":29},"AI","technology","839da5c1-3c34-47e2-9499-f7201640e3ac",{"id":30,"icon":32,"name":33,"slug":34},null,"AI Security","ai-security",[36,41,46],{"category":37},{"id":38,"icon":32,"name":39,"slug":40},"02371804-cf6d-4449-98de-f1a2d4d9b266","Tools","tools",{"category":42},{"id":43,"icon":32,"name":44,"slug":45},"26b0b636-0e31-4db1-bffb-61bdf9f20a58","Supply Chain","supply-chain",{"category":47},{"id":30,"icon":32,"name":33,"slug":34},[],[25,18,21],{"controls":51,"vendors":106},[52,81],{"slug":53,"name":54,"problem":55,"products":56},"vulnerability-management","Vulnerability scanning and prioritisation","You know which systems are vulnerable, and which ones to fix first.",[57,65,73],{"vendor_slug":58,"vendor_name":59,"vendor_logo":60,"product_slug":61,"product_name":62,"url":63,"description":64},"axonius","Axonius","\u002Fvendor-logos\u002Faxonius.png","axonius-exposures","Axonius Exposures","https:\u002F\u002Fwww.axonius.com\u002Fplatform\u002Fexposures","Brings findings from different scanners together with asset and business context, to show what matters most and track fixes to the end.",{"vendor_slug":66,"vendor_name":67,"vendor_logo":68,"product_slug":69,"product_name":70,"url":71,"description":72},"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.",{"vendor_slug":74,"vendor_name":75,"vendor_logo":76,"product_slug":77,"product_name":78,"url":79,"description":80},"qualys","Qualys","\u002Fvendor-logos\u002Fqualys.png","vmdr","Qualys VMDR","https:\u002F\u002Fwww.qualys.com\u002Fapps\u002Fvulnerability-management-detection-response\u002F","Vulnerability management, detection and response with risk-based prioritisation.",{"slug":34,"name":82,"problem":83,"products":84},"AI application security","Prompt injection, data leaks through AI tools and unapproved AI use get caught.",[85,93,101],{"vendor_slug":86,"vendor_name":87,"vendor_logo":88,"product_slug":89,"product_name":90,"url":91,"description":92},"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":94,"vendor_name":95,"vendor_logo":96,"product_slug":97,"product_name":98,"url":99,"description":100},"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":86,"vendor_name":87,"vendor_logo":88,"product_slug":102,"product_name":103,"url":104,"description":105},"boomi-agentstudio","Boomi Agentstudio","https:\u002F\u002Fboomi.com\u002Fplatform\u002Fagentstudio\u002F","Agent builder whose Agent Control Tower registers and monitors Boomi agents and agents on Amazon Bedrock, Microsoft Copilot and other platforms, keeps audit logs and can switch agents off where the provider allows it.",[107],{"slug":108,"name":25,"logo_path":109,"roles":110},"github","\u002Fvendor-logos\u002Fgithub.png",[111],"reported_by",{"slug":113,"title":114,"body":115,"framework_refs":116,"framework_notes":122},"ai-generated-code-fixes-can-introduce-new-vulnerabilities-without-human-oversight","AI-Generated Code Fixes Can Introduce New Vulnerabilities Without Human Oversight","GitHub's Agentic Autofix demonstrates how AI-driven remediation tools can accelerate security backlog resolution, but also expose organizations to new risks when patches are merged without sufficient human review. When an AI-generated fix introduces a new flaw, accountability is split between the tool vendor and the developer who approves the merge — a gap that bad actors could exploit. Automated tools, however effective, can lack the contextual judgment needed to understand complex interdependencies in production code. Organizations must treat AI-suggested fixes as a starting point for human review, not a final authority, and ensure they have processes to validate, test, and own every change that reaches their codebase.",[117,118,119,120,121],"NIST CSF 2.0 PR.PS-04: Software and systems are tested to confirm that security requirements are met","NIS2 Article 21(2)(e)","NIST SP 800-53 SA-11: Developer Testing and Evaluation","CIS Control 16: Application Software Security","NIST CSF 2.0 GV.RO-02: Organizational leadership is responsible and accountable for cybersecurity risk",{"NIS2 Article 21(2)(e)":123,"CIS Control 16: Application Software Security":124,"NIST SP 800-53 SA-11: Developer Testing and Evaluation":125,"NIST CSF 2.0 PR.PS-04: Software and systems are tested to confirm that security requirements are met":126,"NIST CSF 2.0 GV.RO-02: Organizational leadership is responsible and accountable for cybersecurity risk":127},"This point covers security in acquisition, development, and maintenance including vulnerability handling, directly applicable to ensuring AI-generated remediation patches are validated before deployment.","CIS Control 16 calls for managing the security of applications throughout their lifecycle, which includes validating that automated or AI-assisted code changes do not introduce new vulnerabilities.","SA-11 requires developers to test code for security flaws; AI-generated patches must be subject to the same developer-driven security testing requirements as human-authored code.","This category requires testing software changes for security compliance — a step that must be explicitly enforced when AI tools auto-generate patches that could inadvertently introduce new flaws.","The ambiguous accountability between GitHub and the customer when an AI patch causes harm highlights the need for organizations to formally assign internal ownership over third-party AI tooling decisions."]