{
  "predicates": [
    {
      "expanded": "Intentionally-Motivated Failures Summary",
      "description": "Intentional failures wherein the failure is caused by an active adversary attempting to subvert the system to attain her goals – either to misclassify the result, infer private training data, or to steal the underlying algorithm.",
      "value": "intentionally-motivated-failures-summary"
    },
    {
      "description": "Unintentional failures wherein the failure is because an ML system produces a formally correct but completely unsafe outcome.",
      "expanded": "Unintended Failures Summary",
      "value": "unintended-failures-summary"
    }
  ],
  "values": [
    {
      "predicate": "intentionally-motivated-failures-summary",
      "entry": [
        {
          "value": "1-perturbation-attack",
          "expanded": "Perturbation attack",
          "description": "Attacker modifies the query to get appropriate response. It doesn't violate traditional technological notion of access/authorization."
        },
        {
          "value": "2-poisoning-attack",
          "expanded": "Poisoning attack",
          "description": "Attacker contaminates the training phase of ML systems to get intended result. It doesn't violate traditional technological notion of access/authorization."
        },
        {
          "value": "3-model-inversion",
          "expanded": "Model Inversion",
          "description": "Attacker recovers the secret features used in the model by through careful queries. It doesn't violate traditional technological notion of access/authorization."
        },
        {
          "value": "4-membership-inference",
          "expanded": "Membership Inference",
          "description": "Attacker can infer if a given data record was part of the model’s training dataset or not. It doesn't violate traditional technological notion of access/authorization."
        },
        {
          "value": "5-model-stealing",
          "expanded": "Model Stealing",
          "description": "Attacker is able to recover the model through carefully-crafted queries. It doesn't violate traditional technological notion of access/authorization."
        },
        {
          "value": "6-reprogramming-ML-system",
          "expanded": "Reprogramming ML system",
          "description": "Repurpose the ML system to perform an activity it was not programmed for. It doesn't violate traditional technological notion of access/authorization."
        },
        {
          "value": "7-adversarial-example-in-physical-domain",
          "expanded": "Adversarial Example in Physical Domain ",
          "description": "Repurpose the ML system to perform an activity it was not programmed for. It doesn't violate traditional technological notion of access/authorization."
        },
        {
          "value": "8-malicious-ML-provider-recovering-training-data",
          "expanded": "Malicious ML provider recovering training data",
          "description": "Malicious ML provider can query the model used by customer and recover customer’s training data. It does violate traditional technological notion of access/authorization."
        },
        {
          "value": "9-attacking-the-ML-supply-chain",
          "expanded": "Attacking the ML supply chain",
          "description": "Attacker compromises the ML models as it is being downloaded for use. It does violate traditional technological notion of access/authorization."
        },
        {
          "value": "10-backdoor-ML",
          "expanded": "Backdoor ML",
          "description": "Malicious ML provider backdoors algorithm to activate with a specific trigger. It does violate traditional technological notion of access/authorization."
        },
        {
          "value": "10-exploit-software-dependencies",
          "expanded": "Exploit Software Dependencies",
          "description": "Attacker uses traditional software exploits like buffer overflow to confuse/control ML systems. It does violate traditional technological notion of access/authorization."
        }
      ]
    },
    {
      "predicate": "unintended-failures-summary",
      "entry": [
        {
          "value": "12-reward-hacking",
          "expanded": "Reward Hacking",
          "description": "Reinforcement Learning (RL) systems act in unintended ways because of mismatch between stated reward and true reward"
        },
        {
          "value": "13-side-effects",
          "expanded": "Side Effects",
          "description": "RL system disrupts the environment as it tries to attain its goal"
        },
        {
          "value": "14-distributional-shifts",
          "expanded": "Distributional shifts",
          "description": "The system is tested in one kind of environment, but is unable to adapt to changes in other kinds of environment"
        },
        {
          "value": "15-natural-adversarial-examples",
          "expanded": "Natural Adversarial Examples",
          "description": "Without attacker perturbations, the ML system fails owing to hard negative mining"
        },
        {
          "value": "16-common-corruption",
          "expanded": "Common Corruption",
          "description": "The system is not able to handle common corruptions and perturbations such as tilting, zooming, or noisy images"
        },
        {
          "value": "17-incomplete-testing",
          "expanded": "Incomplete Testing",
          "description": "The ML system is not tested in the realistic conditions that it is meant to operate in"
        }
      ]
    }
  ],
  "refs": [
    "https://docs.microsoft.com/en-us/security/failure-modes-in-machine-learning"
  ],
  "version": 1,
  "description": "The purpose of this taxonomy is to jointly tabulate both the of these failure modes in a single place. Intentional failures wherein the failure is caused by an active adversary attempting to subvert the system to attain her goals – either to misclassify the result, infer private training data, or to steal the underlying algorithm. Unintentional failures wherein the failure is because an ML system produces a formally correct but completely unsafe outcome.",
  "expanded": "Failure mode in machine learning.",
  "namespace": "failure-mode-in-machine-learning"
}