Serverless applications are composed of functions triggered by events. Data stores are a common source of event triggers in the cloud, even beyond serverless, such as in Kubernetes. We find trigger latency, the time from event generation to function invocation, to take up to 62% of execution time for common serverless applications. Even though event triggers play a crucial role in serverless performance, the mechanisms driving these triggers are ill-understood. In this paper, we analyze data store trigger mechanisms, define the features that make up these mechanisms, and characterize their performance with TriggerPerf, a benchmarking tool for data store triggers. We implement TriggerPerf on three AWS data stores with built-in trigger support: S3, DynamoDB, and AuroraDB. With TriggerPerf, we demonstrate significant latency, scalability, and elasticity bottlenecks across these data stores. We observe that the trigger latency of AWS data stores is up to$100 \times$higher compared to a reference etcd data store. Moreover, the median tail latency of S3 and AuroraDB is 10x higher when under high load, unlike DynamoDB. The observed variability in performance patterns significantly impacts the reliability of serverless and distributed systems that depend on them, highlighting the critical need for further research into the underlying mechanisms. The tool is open-sourced and is available at https://github.com/atlarge-research/trigger-perf.

Overcoming the threat of coercion attacks in a cryptographic system has been a top priority for system designers since the birth of cyber-security. One way to overcome such a threat is to leverage implicit memory to construct a defense against rubber-hose attacks where the users themselves do not possess conscious knowledge of the trained password. We propose NeuroCrypt, a coercion-resistant authentication system that uses an improved version of the Serial Interception Sequence Learning task, employing additional auditory and haptic modalities backed by concepts borrowed from cognitive psychology. We carefully modify the visual stimuli as well as add auditory and haptic stimuli to improve the implicit learning process, resulting in faster training and longer retention. Moreover, our improvements guarantee that explicit recognition of the trained passwords remains suppressed.