Prompt Injection Defense (pypi.org)

A new project has been launched that offers a lightweight, rule-based prompt injection detector for large language models (LLMs), closely aligned with the OWASP Top 10:2025 security vulnerabilities. This tool serves as a vital safeguard against various types of prompt injections, including jailbreaks and obfuscation techniques, through a zero-configuration and dependency-light approach. By simply inserting a function call into LLM applications, developers can flag potentially unsafe input, enhancing the model's security by detecting injection attempts and unsafe content. This initiative is significant for the AI/ML community as it addresses critical security concerns surrounding LLMs, particularly in a landscape where prompt injection techniques are evolving rapidly. The detector can categorize risks using specific OWASP vulnerabilities, providing developers with actionable feedback on the nature of detected threats. By employing techniques such as leet-speak decoding, character-spacing normalization, and fuzzy matching, the tool effectively identifies and scores suspicious inputs, empowering organizations to better protect their LLM deployments from both existing and emerging attacks. Developers can easily integrate this detection functionality via simple commands, thereby fostering a more secure environment for AI applications.