Online scams continue to cause substantial financial and personal harm. As a result, detection systems based on Large Language Models (LLMs) have been integrated into security products ranging from email gateways and browser extensions to fraud-monitoring dashboards. As this adoption accelerates, a common belief has taken hold: that these models are broadly suitable for scam detection. In this work, we investigate whether LLMs, with their strong capabilities in understanding intent, context, and reasoning, can effectively detect scams across diverse scenarios without task-specific fine-tuning. We curate and release a unique benchmark dataset of real-world scams spanning multiple formats and topics. We evaluate nine LLMs of varying sizes and architectures, examining their performance under different prompting strategies and comparing them to a fine-tuned BERT-based classifier. Our results show that while larger LLMs generally outperform smaller ones, effective prompting substantially boosts the performance of smaller models. Moreover, LLMs are better at generalizing to unseen scams compared to fine-tuned models, suggesting that pre-trained knowledge contributes meaningfully to scam detection. We release our dataset and evaluation framework to facilitate future research in robust scam detection using language models.

Secure aggregation is the distributed task of securely computing a sum of values (or a vector of values) held by a set of parties, revealing only the output (i.e., the sum) in the computation. Existing protocols, such as Prio (NDSI’17), Prio+ (SCN’22), Elsa (S&P’23), and Whisper (S&P’24), support secure aggregation with input validation to ensure inputs belong to a specified domain. However, when malicious servers are present, these protocols primarily guarantee privacy but not input validity. Also, malicious server(s) can cause the protocol to abort. We introduce SCIF, a novel multi-server secure aggregation protocol with input validation, that remains secure even in the presence of malicious actors, provided fewer than one-third of the servers are malicious. Our protocol overcomes previous limitations by providing two key properties: (1) guaranteed output delivery, ensuring malicious parties cannot prevent the protocol from completing, and (2) guaranteed input inclusion, ensuring no malicious party can prevent an honest party’s input from being included in the computation. Together, these guarantees provide strong resilience against denial-of-service attacks. Moreover, SCIF offers these guarantees without increasing client costs over Prio and keeps server costs moderate. We present a robust end-to-end implementation of SCIF and demonstrate the ease with which it can be instrumented by integrating it in a simulated Tor network for privacy-preserving measurement.