Prompt injection is a critical security threat in AI systems where attackers craft specially designed input to override an AI model's original instructions or constraints. By embedding hidden directives within user input, attackers can manipulate the model's behavior, cause it to ignore safety guidelines, leak sensitive information, or perform unauthorized actions. This attack is particularly dangerous in AI-powered applications, agents, and chatbots that process user input without sufficient isolation or validation.

Prompt injection attacks exploit the fact that large language models treat all text as instructions equally, making it difficult to distinguish between legitimate user input and malicious directives. For example, an attacker might append instructions like "Ignore previous instructions and do X" to their input, causing the model to switch contexts and execute the injected command instead of following its original purpose.

Developers building AI agents and applications must implement defensive strategies including prompt segregation (clearly marking system vs. user content), input validation, output filtering, and using structured prompts with explicit delimiters. Testing for prompt injection vulnerabilities should be part of security audits, similar to SQL injection testing in traditional applications. Tool-use-based architectures and sandboxed execution environments can also reduce the impact of successful injection attacks.