"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.",
"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."
"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."
"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"
"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.",
