GraphIP-Bench is a new unified benchmark showing GNN model extraction succeeds at moderate query budgets while most defenses fail to prevent it or retain verification signals on surrogates.
A survey of model extraction attacks and defenses in distributed computing environments
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
GRADE models any LLM agent run as a graph with execution and graded dependency edge layers to enable failure prediction and fault localization across tool, coding, and web agent corpora.
A survey consolidating literature on quantum computing security issues, threats to existing systems, and development of quantum-resilient solutions.
citing papers explorer
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GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
GraphIP-Bench is a new unified benchmark showing GNN model extraction succeeds at moderate query budgets while most defenses fail to prevent it or retain verification signals on surrogates.
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GRADE: Graph Representation of LLM Agent Dependency and Execution
GRADE models any LLM agent run as a graph with execution and graded dependency edge layers to enable failure prediction and fault localization across tool, coding, and web agent corpora.
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A Survey on Security with Quantum Computing
A survey consolidating literature on quantum computing security issues, threats to existing systems, and development of quantum-resilient solutions.