When Ad Networks Misbehave: Understanding Risks of Semi-Drive-By Splash Ads

• URL: http://arxiv.org/abs/2609.09574v1
• Date: Wed, 09 Sep 2026 00:54:43 GMT
• Title: When Ad Networks Misbehave: Understanding Risks of Semi-Drive-By Splash Ads
• Abstract summary: We investigate the mobile splash ads ecosystem, where monetization relies on interaction signals that are difficult to verify end-to-end.<n>We uncover a previously less-known form of fraud at the ad-network layer in which splash ads are triggered not by intentional user actions but by incidental or indirect interactions.<n>By translating non-ad interactions into billable engagement events, ad networks can inflate performance metrics, overcharge advertisers, and erode user trust.
• Score: 13.802323882269995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the mobile splash ads ecosystem, i.e., full-screen advertisements shown at app launch, where monetization relies on interaction signals that are difficult to verify end-to-end. This setting is especially sensitive because incidental touches and sensor-driven callbacks are common yet easy to misattribute as engagement. Prior work has largely framed mobile ad fraud as a publisher-side problem, while some studies attribute fraudulent operations to embedded ad libraries. Yet an important risk remains underexplored: ad SDKs control how interaction signals are interpreted, measured, and reported, creating an opportunity to reinterpret ambiguous user or device signals as valid advertising interactions. We uncover a previously less-known form of fraud at the ad-network layer in which splash ads are triggered not by intentional user actions but by incidental or indirect interactions, which we term semi-drive-by splash ads. By translating non-ad interactions into billable engagement events, ad networks can inflate performance metrics, overcharge advertisers, and erode user trust. To expose this behavior in the wild, we design AdHive, an automated honeypot-like analysis framework that induces evasive splash-ad delivery and landing behaviors under realistic device conditions. AdHive reproduces human-like activity through LLM-generated usage traces and sensor dynamics, enabling execution paths that remain hidden in conventional analysis environments. Our large-scale measurement across thousands of popular Android applications shows that semi-drive-by splash ads are widespread and are often triggered by subtle signals such as minor sensor variations. We further confirm real-world impact by working with one of China's largest advertisers, identifying multiple ad networks engaging in this fraud and leading to enforced repayments of about 4 million Yuan (approximately US$600,000).

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