A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.
In: Proceedings of the 21st International Conference on Mining Software Repositories, pp
4 Pith papers cite this work. Polarity classification is still indexing.
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Analysis of SATD in Dockerfiles shows 27% of admissions and 40% of repayments are coupled to non-Dockerfile artifacts, with coupled events repaid faster overall and external dependencies as a key trigger.
AFGNN detects API misuses in Java code more effectively than prior methods by representing usage as graphs and clustering learned embeddings from self-supervised training.
LLM-driven pairwise comparisons create a partial order on software licenses by permissiveness while leveraging existing taxonomies to identify attributes tied to restrictiveness.
citing papers explorer
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Protecting On-Device AI Inference: A Systematic Review of Attacks and Defence Mechanisms
A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.
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Beyond the Tip of the Iceberg: Understanding SATD in Dockerfiles through the Lens of Co-evolution
Analysis of SATD in Dockerfiles shows 27% of admissions and 40% of repayments are coupled to non-Dockerfile artifacts, with coupled events repaid faster overall and external dependencies as a key trigger.
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AFGNN: API Misuse Detection using Graph Neural Networks and Clustering
AFGNN detects API misuses in Java code more effectively than prior methods by representing usage as graphs and clustering learned embeddings from self-supervised training.
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Partially ordering software licenses
LLM-driven pairwise comparisons create a partial order on software licenses by permissiveness while leveraging existing taxonomies to identify attributes tied to restrictiveness.