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{
"predicates": [
{
"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"
},
{
"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"
}
],
"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"
}
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