arXiv:2606. 26707v1 Announce Type: cross Abstract: Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors.
arXiv:2601. 12349v3 Announce Type: replace-cross Abstract: Large multimodal model powered GUI agents are emerging as high-privilege operators on mobile platforms, entrusted to perceive screen content and inject inputs across application boundaries.
arXiv:2512. 20872v2 Announce Type: replace-cross Abstract: Function call graphs (FCGs) have emerged as a powerful abstraction for malware detection, capturing the behavioral structure of applications beyond surface-level signatures.
arXiv:2609.23980v1 Announce Type: cross Abstract: AI agents now report vulnerabilities faster than maintainers can review them. Reports often depend on security properties specific to the application...
arXiv:2605. 09028v3 Announce Type: replace Abstract: Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perform poorly on applications from another.
arXiv:2503.11841v2 Announce Type: replace-cross Abstract: Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as truste...
MobileWorldSafety is a benchmark that evaluates the safety of large language model–powered GUI agents on Android by exposing them to 142 real-world risk tasks involving environmental injection attacks. The benchmark uses a two‑stage verification pipeline—rule‑based checks for clear cases and an LLM judge for ambiguous ones—to distinguish safety failures from capability failures. Experiments on six agents show high vulnerability, with attack success rates between 40.4% and 66.9%, highlighting that current agents often fail to remain safe when faced with adversarial content presented as normal mobile context.
arXiv:2608. 03250v1 Announce Type: cross Abstract: The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications.
The paper introduces Replicant, a deep reinforcement learning framework that learns to evade malware detectors under a strict label‑only black‑box threat model. Replicant generates reusable policies for modifying malware samples and deciding when to query the target, and it transfers across different samples, detectors, and feature spaces. In experiments on seven Android malware detectors and three feature spaces, Replicant achieves a mean attack success rate of 78.8%, outperforming state‑of‑the‑art methods by 20.9%–39.2% and providing a stronger signal for adversarial training to harden detectors.
arXiv:2508. 10031v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) have shown significant advancements in performance, various jailbreak attacks have posed growing safety and ethical risks.
The paper introduces a new benchmark for assessing out-of-distribution robustness in graph-based Android malware classifiers, highlighting that current models drop up to 45% accuracy on unseen malware variants. It presents two scenarios—MalNet-Tiny-Common for covariate shift and MalNet-Tiny-Distinct for domain shift—and identifies a limitation in existing benchmarks that rely solely on structure-only function call graphs. To address this, the authors propose a semantic enrichment framework that augments graph topology with function-level attributes and LLM-based code embeddings, demonstrating that this data-centric approach improves robustness under distribution shift and complements model-based methods.
arXiv:2604. 23025v2 Announce Type: replace-cross Abstract: Android malware detectors built with machine learning often suffer from temporal bias: models are trained and evaluated without respecting apps' actual release times, inflating accuracy and weakening real-world robustness.
arXiv:2507. 18313v2 Announce Type: replace Abstract: Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated.