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Threat actors abuse open source AI platform Cairn to automate attacks on e-commerce sites, reportedly breaching over 100 organizations

1 fonte(s) · 1 atualização(ões) · publicado 20/10/2026 09:47 · coletado há 11 horas

Threat actors are abusing the open source AI platform Cairn, an agent-coordination engine usable as an autonomous penetration testing tool, to automate attacks on hundreds of websites worldwide. AI security firm Norforge found the activity after threat actors left their Cairn panels exposed, following a Gambit Security report on attackers using three AI harnesses against online retailers. Norforge identified three malicious clusters focused on persistent access and financial theft. Reuters reports that the largest cluster breached over 100 companies, including John Deere and SharkNinja, although Norforge did not name any victims.

Technical Details

  • Cairn treats each task as a project with an origin and a goal. Agents work step by step and post intents and findings to a shared blackboard.
  • Cluster 1 (Chinese-language): over 1,100 attack projects across 14 dashboards, targeting e-commerce and payment infrastructure in the US, Japan and Australia. The goal appears to be persistent access to large platforms via webshells, likely to be monetized later. Targets were ranked by traffic and validated with Microsoft's Playwright browser automation framework before being fed to Cairn.
  • Cluster 2 (English-language): 10 exposed panels, focused on credit card and cryptocurrency theft from merchant payment services, gray-market payment sites and pharmaceutical platforms. Several targets are African commerce sites, and Norforge believes the actor is based in Africa.
  • Cluster 3: one panel with 59 projects against all types of e-commerce sites, including very large US platforms, aiming at shell access plus admin panel and database access.
  • Attackers succeeded in roughly 10% of attempts. Hundreds of secrets were found in the exposed panel data, and 11% were verified as valid.

Remediation

The article gives no specific remediation guidance.

Linha do tempo

  1. securityweek.com ↗ · pub. 20/10/2026 09:47 · coletado 20/10 11:00 primeiro registro

    Threat actors abuse open source AI platform Cairn to automate attacks on e-commerce sites, reportedly breaching over 100 organizations

    ler a análise desta fonte

    Threat actors are abusing the open source AI platform Cairn, an agent-coordination engine usable as an autonomous penetration testing tool, to automate attacks on hundreds of websites worldwide. AI security firm Norforge found the activity after threat actors left their Cairn panels exposed, following a Gambit Security report on attackers using three AI harnesses against online retailers. Norforge identified three malicious clusters focused on persistent access and financial theft. Reuters reports that the largest cluster breached over 100 companies, including John Deere and SharkNinja, although Norforge did not name any victims.

    Technical Details

    • Cairn treats each task as a project with an origin and a goal. Agents work step by step and post intents and findings to a shared blackboard.
    • Cluster 1 (Chinese-language): over 1,100 attack projects across 14 dashboards, targeting e-commerce and payment infrastructure in the US, Japan and Australia. The goal appears to be persistent access to large platforms via webshells, likely to be monetized later. Targets were ranked by traffic and validated with Microsoft's Playwright browser automation framework before being fed to Cairn.
    • Cluster 2 (English-language): 10 exposed panels, focused on credit card and cryptocurrency theft from merchant payment services, gray-market payment sites and pharmaceutical platforms. Several targets are African commerce sites, and Norforge believes the actor is based in Africa.
    • Cluster 3: one panel with 59 projects against all types of e-commerce sites, including very large US platforms, aiming at shell access plus admin panel and database access.
    • Attackers succeeded in roughly 10% of attempts. Hundreds of secrets were found in the exposed panel data, and 11% were verified as valid.

    Remediation

    The article gives no specific remediation guidance.