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Large language models (LLMs) can expose memorized training sequences under prefix-based extraction: given a prefix from a training example, the model may assign high probability to the original continuation. In deployed systems, however, prefixes are rarely evaluated in isolation. They often appear together with instru...
Coverage feedback is an important source of guidance for fuzzing. However, obtaining such feedback normally requires application-level instrumentation that is specific to the language and runtime of the application. Given that modern web applications span multiple languages and runtimes, this application-level instrume...
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a reliable and cost-effective technique of grounding the generative capabilities of Multimodal Large Language Models (MLLMs) into relevant, up-to-date, external knowledge. Despite presenting several benefits, such as reducing hallucinatory behavior, they a...
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