AI2030 Responsible AI Tools Marketplace

Key Takeaways

Explainability can create new security vulnerabilities: The paper demonstrates that XAI tools designed to make AI systems more transparent and trustworthy can paradoxically be exploited by adversaries to attack those same systems more effectively, revealing a tension between transparency and security in AI deployment.
Attackers can fool AI systems by swapping key features: XSub works by identifying the most important features in a model's decision-making (using XAI explanations) and strategically replacing them with features from examples of different categories, causing the model to make incorrect predictions while keeping changes subtle enough to avoid detection.
These attacks are efficient and practical: The attack requires only a constant number of queries to the model, making it cost-effective and scalable—a concerning finding that suggests such attacks could be deployed against real-world AI systems without requiring extensive computational resources or repeated testing.
There's a controllable trade-off between effectiveness and detectability: The flexibility to adjust how much of the original sample is modified allows attackers to balance between making the attack work reliably and keeping it stealthy, meaning defenders cannot assume that obvious manipulations are the only threat.
The findings highlight urgent need for defensive measures: As organizations increasingly adopt XAI for responsible AI practices, this research underscores the importance of developing security safeguards alongside explainability tools to prevent malicious exploitation.

Abstract

Despite its significant benefits in enhancing the transparency and trustworthiness of artificial intelligence (AI) systems, explainable AI (XAI) has yet to reach its full potential in real-world applications. One key challenge is that XAI can unintentionally provide adversaries with insights into black-box models, inevitably increasing their vulnerability to various attacks. In this paper, we develop a novel explanation-driven adversarial attack against black-box classifiers based on feature substitution, called XSub. The key idea of XSub is to strategically replace important features (identified via XAI) in the original sample with corresponding important features from a "golden sample" of a different label, thereby increasing the likelihood of the model misclassifying the perturbed sample. The degree of feature substitution is adjustable, allowing us to control how much of the original samples information is replaced. This flexibility effectively balances a trade-off between the attacks effectiveness and its stealthiness. XSub is also highly cost-effective in that the number of required queries to the prediction model and the explanation model in conducting the attack is in O(1). In addition, XSub can be easily extended to launch backdoor attacks in case the attacker has access to the models training data. Our evaluation demonstrates that XSub is not only effective and stealthy but also cost-effective, enabling its application across a wide range of AI models.

Key Takeaways

Explainability can create new security vulnerabilities: The paper demonstrates that XAI tools designed to make AI systems more transparent and trustworthy can paradoxically be exploited by adversaries to attack those same systems more effectively, revealing a tension between transparency and security in AI deployment.
Attackers can fool AI systems by swapping key features: XSub works by identifying the most important features in a model's decision-making (using XAI explanations) and strategically replacing them with features from examples of different categories, causing the model to make incorrect predictions while keeping changes subtle enough to avoid detection.
These attacks are efficient and practical: The attack requires only a constant number of queries to the model, making it cost-effective and scalable—a concerning finding that suggests such attacks could be deployed against real-world AI systems without requiring extensive computational resources or repeated testing.
There's a controllable trade-off between effectiveness and detectability: The flexibility to adjust how much of the original sample is modified allows attackers to balance between making the attack work reliably and keeping it stealthy, meaning defenders cannot assume that obvious manipulations are the only threat.
The findings highlight urgent need for defensive measures: As organizations increasingly adopt XAI for responsible AI practices, this research underscores the importance of developing security safeguards alongside explainability tools to prevent malicious exploitation.

Abstract

XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links