The field of large language models (LLMs) is rapidly evolving, with a growing focus on safeguarding these models against various threats. Recent research has highlighted the importance of developing effective defense mechanisms to prevent jailbreaking, data leakage, and other types of attacks. One notable trend is the development of novel frameworks and methods that leverage the models' internal reasoning capabilities to perform self-protection. For instance, some approaches utilize meta-cognitive and arbitration modules to enable LLMs to evaluate and regulate their own outputs autonomously. Other research has focused on improving the detection of adversarial inputs, using techniques such as latent space features of contextual co-occurrence tensors. Noteworthy papers in this area include R1-ACT, which proposes a simple and efficient post-training method to explicitly trigger safety knowledge in LLMs, and CyGATE, which presents a game-theoretic framework for modeling attacker-defender interactions. Additionally, papers like Activation-Guided Local Editing and DACTYL have introduced innovative methods for detecting and mitigating jailbreak attacks, while LeakSealer and ReasoningGuard have proposed novel defense mechanisms for preventing data leakage and promoting safe reasoning processes.