We introduce in-context denoising, a task that refines the connection between attention-based architectures and dense associative memory (DAM) networks, also known as modern Hopfield networks. Using a Bayesian framework, we show theoretically and empirically that certain restricted denoising problems can be solved optimally even by a single-layer transformer. We demonstrate that a trained attention layer processes each denoising prompt by performing a single gradient descent update on a context-aware DAM energy landscape, where context tokens serve as associative memories and the query token acts as an initial state. This one-step update yields better solutions than exact retrieval of either a context token or a spurious local minimum, providing a concrete example of DAM networks extending beyond the standard retrieval paradigm. Overall, this work solidifies the link between associative memory and attention mechanisms first identified by Ramsauer et al., and demonstrates the relevance of associative memory models in the study of in-context learning.

This paper describes the design and implementation of protocol scrubbers. Protocol scrubbers are transparent, interposed mechanisms for explicitly removing network scans and attacks at various protocol layers. The transport scrubber supports downstream passive network-based intrusion detection systems by converting ambiguous network flows into well-behaved flows that are unequivocally interpreted by all downstream endpoints. The fingerprint scrubber restricts an attacker's ability to determine the operating system of a protected host. As an example, this paper presents the implementation of a TCP scrubber that eliminates insertion and evasion attacks-attacks that use ambiguities to subvert detection-on passive network-based intrusion detection systems, while preserving high performance. The TCP scrubber is based on a novel, simplified state machine that performs in a fast and scalable manner. The fingerprint scrubber is built upon the TCP scrubber and removes additional ambiguities from flows that can reveal implementation-specific details about a host's operating system.