{
  "affects": {
    "artifacts": [
      {
        "name": "Kaspersky\u0027s Antimalware ML Model",
        "type": "System"
      }
    ],
    "deployer": [
      "Kaspersky\u0027s Antimalware ML Model"
    ],
    "developer": []
  },
  "credit": null,
  "data_type": "AVID",
  "data_version": "0.2",
  "description": {
    "lang": "eng",
    "value": "Cloud storage and computations have become popular platforms for deploying ML malware detectors.\nIn such cases, the features for models are built on users\u0027 systems and then sent to cybersecurity company servers.\nThe Kaspersky ML research team explored this gray-box scenario and showed that feature knowledge is enough for an adversarial attack on ML models.\n\nThey attacked one of Kaspersky\u0027s antimalware ML models without white-box access to it and successfully evaded detection for most of the adversarially modified malware files."
  },
  "impact": {
    "avid": {
      "lifecycle_view": [
        "L06: Deployment"
      ],
      "risk_domain": [
        "Security"
      ],
      "sep_view": [
        "S0403: Adversarial Example"
      ],
      "taxonomy_version": "0.2"
    }
  },
  "last_modified_date": "2023-03-31",
  "metadata": {
    "vuln_id": "AVID-2023-V014"
  },
  "problemtype": {
    "classof": "ATLAS Case Study",
    "description": {
      "lang": "eng",
      "value": "Confusing Antimalware Neural Networks"
    },
    "type": "Advisory"
  },
  "published_date": "2023-03-31",
  "references": [
    {
      "label": "Confusing Antimalware Neural Networks",
      "type": "source",
      "url": "https://atlas.mitre.org/studies/AML.CS0014"
    },
    {
      "label": "Article, \"How to confuse antimalware neural networks. Adversarial attacks and protection\"",
      "type": "source",
      "url": "https://securelist.com/how-to-confuse-antimalware-neural-networks-adversarial-attacks-and-protection/102949/"
    }
  ],
  "reports": null
}