Prompt Injection
Prompt injection is a security vulnerability where malicious instructions embedded in user input override or manipulate an AI system's intended behavior. Defending against prompt injection is an active area of AI security research.
Understanding Prompt Injection
Prompt injection is a security vulnerability where malicious inputs are crafted to override or manipulate the intended instructions given to a large language model, causing it to ignore safety guidelines, leak system prompts, or perform unintended actions. Similar in concept to SQL injection attacks in traditional software, prompt injection exploits the fact that language models process user inputs and system instructions within the same context. Direct injection involves a user explicitly instructing the model to ignore previous instructions, while indirect injection embeds malicious prompts in external content the model retrieves during retrieval-augmented generation. This vulnerability poses significant risks for AI applications handling sensitive data or performing automated actions. Defenses include input sanitization, output filtering, separate processing of instructions and user content, and red-teaming exercises. Prompt injection remains one of the most active areas of research in AI safety and AI alignment.
Category
AI Ethics & Safety
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Adversarial Attack
An adversarial attack is a technique that creates deliberately crafted inputs designed to fool a machine learning model into making incorrect predictions. These attacks reveal vulnerabilities in AI systems and are critical to AI safety research.
Adversarial Training
Adversarial training is a defense strategy that improves model robustness by including adversarial examples in the training data. The model learns to correctly classify both normal and adversarially perturbed inputs.
AI Alignment
AI alignment is the research field focused on ensuring that AI systems pursue goals and behaviors consistent with human values and intentions. Alignment is considered one of the most important challenges in AI safety.
AI Ethics
AI ethics is the branch of ethics that examines the moral implications of developing and deploying artificial intelligence systems. It addresses fairness, transparency, privacy, accountability, and the societal impact of AI technology.
AI Safety
AI safety is the interdisciplinary field focused on ensuring AI systems operate reliably, beneficially, and without causing unintended harm. It encompasses alignment, robustness, interpretability, and governance research.
Bias in AI
Bias in AI refers to systematic errors or unfair outcomes in machine learning models that arise from biased training data, flawed assumptions, or problematic design choices. Addressing AI bias is essential for building fair and equitable systems.
Constitutional AI
Constitutional AI is an approach developed by Anthropic that trains AI systems to be helpful, harmless, and honest using a set of written principles. The model critiques and revises its own outputs based on these constitutional rules.
Deepfake
A deepfake is AI-generated synthetic media that convincingly replaces a person's likeness, voice, or actions in images, audio, or video. Deepfakes raise significant concerns about misinformation and identity fraud.