Aligning language models (LMs) to human preferences has emerged as a critical pursuit,
enabling these models to better serve diverse user needs. Existing methods primarily focus …

Cyber-attack attribution is an important process that allows experts to put in place attacker-
oriented countermeasures and legal actions. The analysts mainly perform attribution …

Abstention, the refusal of large language models (LLMs) to provide an answer, is
increasingly recognized for its potential to mitigate hallucinations and enhance safety in …

Large language models (LLMs) possess immense capabilities but are susceptible to
malicious exploitation. To mitigate the risk, safety alignment is employed to align LLMs with …

Large language models (LLMs) have transformed the field of natural language processing,
but they remain susceptible to jailbreaking attacks that exploit their capabilities to generate …

Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-
broken by fine-tuning on a dataset mixed with harmful data. First time in the literature, we …

The existing safety alignment of Large Language Models (LLMs) is found fragile and could
be easily attacked through different strategies, such as through fine-tuning on a few harmful …

What makes a good Large Language Model (LLM)? That it performs well on the relevant
benchmarks--which hopefully measure, with some validity, the presence of capabilities that …

Large language models (LLMs) are often fine-tuned for use on downstream tasks, though
this can degrade capabilities learned during previous training. This phenomenon, often …