We first prove the first inequality using Jensen's inequality, which states that for a real-valued, convex Next, we leverage the property of inequalities to prove the second inequality. However, this method has limited effectiveness in scenarios with severe domain shifts between the source and target domains. Directly taking source risk as target risk is unreliable due to domain distribution shifts between domains. This work was completed while Dapeng ( lhxxhb15@gmail.com) Subsequently, Reverse V alidation performs a reversed adaptation from the pseudo-labeled target to the source and utilizes the source risk in this reversed adaptation task for validation.

Existing approaches can be categorized into two types. The first type involves leveraging labeled source data for target-domain model selection [9,14-16]. The second type designs unsupervised metrics based on priors of the learned target-domain structure and utilizes the metrics for model selection[17,19,18,20].

Traditional IM approaches consider each user/node independently as a potential target customer.

PCWorld reports that OpenAI is launching new security features for ChatGPT, including Lockdown Mode and Elevated Risk labels to combat growing threats. Lockdown Mode restricts external interactions and disables web browsing for high-privacy users, while risk labels clearly mark potentially dangerous features. These updates specifically address prompt injection attacks where malicious prompts attempt to trick the AI into performing harmful actions. OpenAI is launching two new security features in ChatGPT to address growing threats to its AI systems, according to a recent blog post . As AI services increasingly connect to wider parts of the web and more external apps, the risk of so-called "prompt injection attacks" also increases.

Real-world multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation.