AVID-2023-V010

Vulnerability from avid – Published: 2023-03-31 – Updated: 2023-03-31 ATLAS Case Study
Summary
The Microsoft AI Red Team performed a red team exercise on an internal Azure service with the intention of disrupting its service. This operation had a combination of traditional ATT&CK enterprise techniques such as finding valid account, and exfiltrating data -- all interleaved with adversarial ML specific steps such as offline and online evasion examples.
Risk domain
Security
SEP view
S0100: Software Vulnerability, S0301: Information Leak, S0403: Adversarial Example
Lifecycle
L06: Deployment
Organisations
Internal Microsoft Azure Service (deployer)
Affected artifacts
Artifact Type
Internal Microsoft Azure Service System
References
URL Label
https://atlas.mitre.org/studies/AML.CS0010 Microsoft Azure Service Disruption
To clipboard 

{
  "affects": {
    "artifacts": [
      {
        "name": "Internal Microsoft Azure Service",
        "type": "System"
      }
    ],
    "deployer": [
      "Internal Microsoft Azure Service"
    ],
    "developer": []
  },
  "credit": null,
  "data_type": "AVID",
  "data_version": "0.2",
  "description": {
    "lang": "eng",
    "value": "The Microsoft AI Red Team performed a red team exercise on an internal Azure service with the intention of disrupting its service. This operation had a combination of traditional ATT\u0026CK enterprise techniques such as finding valid account, and exfiltrating data -- all interleaved with adversarial ML specific steps such as offline and online evasion examples."
  },
  "impact": {
    "avid": {
      "lifecycle_view": [
        "L06: Deployment"
      ],
      "risk_domain": [
        "Security"
      ],
      "sep_view": [
        "S0100: Software Vulnerability",
        "S0301: Information Leak",
        "S0403: Adversarial Example"
      ],
      "taxonomy_version": "0.2"
    }
  },
  "last_modified_date": "2023-03-31",
  "metadata": {
    "vuln_id": "AVID-2023-V010"
  },
  "problemtype": {
    "classof": "ATLAS Case Study",
    "description": {
      "lang": "eng",
      "value": "Microsoft Azure Service Disruption"
    },
    "type": "Advisory"
  },
  "published_date": "2023-03-31",
  "references": [
    {
      "label": "Microsoft Azure Service Disruption",
      "type": "source",
      "url": "https://atlas.mitre.org/studies/AML.CS0010"
    }
  ],
  "reports": null
}


Log in or create an account to share your comment.




Tags
Taxonomy of the tags.





Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.


Detection rules are retrieved from Rulezet.