Recent research indicates that large language models (LLMs) may exhibit behavioral self-awareness after being fine-tuned on specific tasks, such as generating insecure code. A study led by researchers from Truthful AI and the University of Toronto suggests that these models can recognize their learned behaviors without explicit training. Another paper from OpenAI discusses safety and security concerns, introducing the concept of an 'LMP attack' which raises questions about privacy in LLM interactions. Additionally, a separate study highlights the potential for LLMs to defend against jailbreaking attacks, suggesting they can identify backdoor placements and explain deviations from expected outputs. These findings have sparked discussions about the implications of LLMs' self-awareness and their ability to maintain security and privacy.