Lindsey Graham, who has died aged 71, was a political survivor. His career as a Republican senator served as a telling barometer for the dramatically changing climate in his political party - and America - in the Donald Trump era. While there were certain issues central to Graham's political identity - including a hawkish foreign policy that focused on containing Russian global ambitions, support for Israel and regime change in Iran - his 23-year career in the Senate was marked by a willingness to adapt to the gale-force change of political winds that accompanied Trump's rise to power. Shortly after being elected to represent South Carolina in the Senate in 2002, Graham became a close ally of Senator John McCain, the Arizona Republican who, while a staunch conservative, developed a national reputation for political independence. When Graham ran for president in 2015, the idea of cooling partisan tensions and working with political opponents was one of his central messages. If I get to be president, we're going to open up a bar in the White House, Graham said.

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Consider this prompt "Draw a unicorn with two horns". Should large language models (LLMs) recognize that a unicorn has only one horn by definition and ask users for clarifications, or proceed to generate something anyway? We introduce concept incongruence to capture such phenomena where concept boundaries clash with each other, either in user prompts or in model representations, often leading to under-specified or mis-specified behaviors. In this work, we take the first step towards defining and analyzing model behavior under concept incongruence. Focusing on temporal boundaries in the ROLE-PLAY setting, we propose three behavioral metrics--abstention rate, conditional accuracy, and answer rate--to quantify model behavior under incongruence due to the role's death. We show that models fail to abstain after death and suffer from an accuracy drop compared to the NON-ROLE-PLAY setting. Through probing experiments, we identify two main causes: (i) unreliable encoding of the "death" state across different years, leading to unsatisfactory abstention behavior, and (ii) role playing causes shifts in the model's temporal representations, resulting in accuracy drops. We leverage these insights to improve consistency in the model's abstention and answer behaviors. Our findings suggest that concept incongruence leads to unexpected model behaviors and point to future directions on improving model behavior under concept incongruence.1

Despite their superior performance on a wide range of domains, large language models (LLMs) remain vulnerable to misuse for generating harmful content, a risk that has been further amplified by various jailbreak attacks. Existing jailbreak attacks mainly follow sequential logic, where LLMs understand and answer each given task one by one. However, concurrency, a natural extension of the sequential scenario, has been largely overlooked. In this work, we first propose a wordlevel method to enable task concurrency in LLMs, where adjacent words encode divergent intents. Although LLMs maintain strong utility in answering concurrent tasks, which is demonstrated by our evaluations on mathematical and general question-answering benchmarks, we notably observe that combining a harmful task with a benign one significantly reduces the probability of it being filtered by the guardrail, showing the potential risks associated with concurrency in LLMs. Based on these findings, we introduce JAIL-CON, an iterative attack framework that JAILbreaks LLMs via task CONcurrency. Experiments on widely-used LLMs demonstrate the strong jailbreak capabilities of JAIL-CON compared to existing attacks. Furthermore, when the guardrail is applied as a defense, compared to the sequential answers generated by previous attacks, the concurrent answers in our JAIL-CONexhibit greater stealthiness and are less detectable by the guardrail, highlighting the unique feature of task concurrency in jailbreaking LLMs.1 Disclaimer: This paper contains unsafe information.