Toward Trustworthy AI: Multi-Target Adversarial Attacks and Robust Defenses for Continuous Data Summarization
arXiv:2606.11804v1 Announce Type: cross Abstract: Trustworthy AI requires reliable data-processing pipelines, not only robust downstream predictive models. As an upstream component, data summarization determines which information is retained and passed to subsequent learning or decision modules. Therefore, adversarial perturbations to the summarization process can compromise trustworthy AI in an upstream manner: they may alter the selected summary, reduce its representativeness, and further degrade the utility of subsequent learning tasks. In this paper, we study adversarial attacks on continu
The accelerating deployment of AI systems across critical infrastructure and decision-making necessitates a robust understanding and mitigation of adversarial vulnerabilities to ensure trustworthiness and reliability.
Understanding multi-target adversarial attacks on upstream data summarization is crucial for strategic readers as it highlights fundamental weaknesses in AI pipelines, potentially compromising data integrity and downstream decisions.
The focus expands from securing predictive models to securing the entire AI data processing pipeline, including upstream components, requiring more comprehensive defense strategies.
- · AI security researchers
- · Cybersecurity firms
- · Robust AI platform providers
- · Developers of unhardened AI systems
- · Organizations relying on insecure AI pipelines
- · Data summarization solution providers
Increased investment in R&D for AI robustness and security, particularly for foundational data processing layers.
New regulatory and compliance standards for 'trustworthy AI' that encompass the entire pipeline, not just the final model.
Elevated risk of nation-state or sophisticated actor exploitation of AI supply chain vulnerabilities to manipulate information or critical systems.
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Read at arXiv cs.LG