Hidden prompts refer to subtle or obscured instructions embedded within data that can influence the behavior of an artificial intelligence model without being explicitly visible to a user. These prompts might be unintentionally present or deliberately concealed to manipulate outputs. Their presence can impact model fairness or security. They represent covert influences.
Context
In the context of AI applications within digital asset markets or blockchain security, hidden prompts could potentially skew algorithmic trading decisions or compromise the integrity of automated auditing systems. Detecting and mitigating these latent influences is a critical area of research for ensuring the trustworthiness of AI in financial technology. Discussions often focus on explainable AI and robust adversarial training to counter such covert manipulations.
A new principled detection framework, PhantomLint, neutralizes hidden LLM prompts in structured documents, securing AI-assisted processing against injection attacks.
We use cookies to personalize content and marketing, and to analyze our traffic. This helps us maintain the quality of our free resources. manage your preferences below.
Detailed Cookie Preferences
This helps support our free resources through personalized marketing efforts and promotions.
Analytics cookies help us understand how visitors interact with our website, improving user experience and website performance.
Personalization cookies enable us to customize the content and features of our site based on your interactions, offering a more tailored experience.
