arXiv:2607.02903v1 Announce Type: cross Abstract: Explainability is central to building trustworthy AI, yet explanation interfaces can inadvertently provide adversaries with an expanded privacy-related attack surfaces. Recent studies show that advanced membership-inference attacks succeed by exploiting confidence-drop trajectories, induced through attribution-guided perturbations, as discriminative features, rather than directly using confidence scores or explanation vectors. Existing defenses against membership inference fail to directly mitigate such explanation-driven attacks. In this work,
The increasing deployment of AI systems highlights the tension between explainability, crucial for trust, and privacy, a growing concern in AI ethics and regulation.
This research addresses a critical vulnerability in AI systems where attempts to explain decisions can inadvertently create new attack vectors for privacy infringement, affecting anyone deploying or relying on AI.
Existing protections against membership inference are shown to be inadequate against 'explainability-driven attacks,' requiring new defense mechanisms to maintain privacy while ensuring AI interpretability.
- · AI security researchers
- · Developers of privacy-preserving AI
- · Users of secure AI systems
- · AI systems relying on naive explainability methods
- · Organizations with weak AI privacy safeguards
- · Adversaries exploiting explainability for privacy breaches
AI developers will need to integrate new defenses to prevent explanation-driven privacy attacks.
Increased scrutiny of AI explanation methods will lead to more robust and privacy-aware explainable AI (XAI) frameworks.
The development of truly privacy-preserving yet explainable AI could accelerate AI adoption in sensitive sectors.
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Read at arXiv cs.AI