Blackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networks
arXiv:2606.29240v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have achieved strong performance in modeling complex graph-structured data with multiple node and relation types. However, their robustness under realistic black-box adversarial settings remains insufficiently explored. Existing attacks on HGNNs usually assume access to model gradients, soft prediction scores, or the complete graph structure, which is often unavailable when HGNN-based services are deployed as closed systems. In this paper, we propose Blackknife, a hard-label, query-limited, and structur
The increasing deployment of advanced AI models in sensitive applications necessitates robust testing for adversarial vulnerabilities, particularly as these systems become more opaque and integrated into critical infrastructure.
This research highlights the growing risk of sophisticated black-box attacks on crucial AI systems, underscoring the need for advanced defensive measures to ensure their reliability and security in closed deployments.
The development of hard-label, query-limited attack methods forces AI developers to confront more realistic and challenging adversarial scenarios, potentially accelerating the development of more resilient AI architectures.
- · AI security researchers
- · Cybersecurity companies
- · Organizations deploying robust AI defenses
- · Developers of vulnerable HGNN systems
- · Organizations relying on insecure AI models
- · Entities with weak AI security protocols
Increased investment in AI robustness and adversarial defense research and development.
New regulatory requirements or industry standards for the adversarial resilience of deployed AI systems.
A shift towards inherently more interpretable and robust AI architectures to mitigate black-box attack vectors.
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Read at arXiv cs.LG