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Register a new userSecure-UCB: Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification
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Anshuka Rangi
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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This paper studies bandit algorithms under data poisoning attacks in a bounded reward setting. We consider a strong attacker model in which the attacker can observe both the selected actions and their corresponding rewards, and can contaminate the rewards with additive noise. We show that emph{any} bandit algorithm with regret $O(log T)$ can be forced to suffer a regret $Omega(T)$ with an expected amount of contamination $O(log T)$. This amount of contamination is also necessary, as we prove that there exists an $O(log T)$ regret bandit algorithm, specifically the classical UCB, that requires $Omega(log T)$ amount of contamination to suffer regret $Omega(T)$. To combat such poising attacks, our second main contribution is to propose a novel algorithm, Secure-UCB, which uses limited emph{verification} to access a limited number of uncontaminated rewards. We show that with $O(log T)$ expected number of verifications, Secure-UCB can restore the order optimal $O(log T)$ regret emph{irrespective of the amount of contamination} used by the attacker. Finally, we prove that for any bandit algorithm, this number of verifications $O(log T)$ is necessary to recover the order-optimal regret. We can then conclude that Secure-UCB is order-optimal in terms of both the expected regret and the expected number of verifications, and can save stochastic bandits from any data poisoning attack.
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We study a security threat to reinforcement learning where an attacker poisons the learning environment to force the agent into executing a target policy chosen by the attacker. As a victim, we consider RL agents whose objective is to find a policy that maximizes reward in infinite-horizon problem settings. The attacker can manipulate the rewards and the transition dynamics in the learning environment at training-time, and is interested in doing so in a stealthy manner. We propose an optimization framework for finding an optimal stealthy attack for different measures of attack cost. We provide lower/upper bounds on the attack cost, and instantiate our attacks in two settings: (i) an offline setting where the agent is doing planning in the poisoned environment, and (ii) an online setting where the agent is learning a policy with poisoned feedback. Our results show that the attacker can easily succeed in teaching any target policy to the victim under mild conditions and highlight a significant security threat to reinforcement learning agents in practice.
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Certifiers for neural networks have made great progress towards provable robustness guarantees against evasion attacks using adversarial examples. However, introducing certifiers into deep learning systems also opens up new attack vectors, which need to be considered before deployment. In this work, we conduct the first systematic analysis of training time attacks against certifiers in practical application pipelines, identifying new threat vectors that can be exploited to degrade the overall system. Using these insights, we design two backdoor attacks against network certifiers, which can drastically reduce certified robustness when the backdoor is activated. For example, adding 1% poisoned data points during training is sufficient to reduce certified robustness by up to 95 percentage points, effectively rendering the certifier useless. We analyze how such novel attacks can compromise the overall systems integrity or availability. Our extensive experiments across multiple datasets, model architectures, and certifiers demonstrate the wide applicability of these attacks. A first investigation into potential defenses shows that current approaches only partially mitigate the issue, highlighting the need for new, more specific solutions.
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