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Several open research questions are identified, including how to efficiently record incidents and how to determine whether vulnerabilities and incidents generalize, and privacy requirements are summarized and research directions for the secure and trustworthy deployment of AI agents are outlined.
Industrial Windows malware detectors are commonly described as Compound AI Systems composed of multiple heterogeneous components, including rule-based mechanisms as well as machine-learning-based static and dynamic analyses. However, due to industrial secrecy and limited public disclosure, the internal architectures of...
A unified evaluation framework based on a comprehensive pool of minimum-norm attacks and robustness--perturbation curves across $\ell_0, $\ell_1, $\ell_2$ and $\ell_\infty$ norms", and proposes the Defense Optimality Index to rank defenses by their gap to the defense frontier.
A novel deterministic certification schema based on (de)randomized smoothing that guarantees that each chunk either contains or does not contain an adversarial perturbation, enabling it to handle manipulations occurring at arbitrary locations within the program and compute deterministic estimates of the perturbation ma...
ExE-Bench assesses performance, temporal and adversarial robustness, and computational overhead, aggregating them into a single score for direct and fair model comparison, and highlights how evaluations conducted only after deployment are suboptimal and unable to provide a complete picture of their performance.
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