CSL-Core: Formally Verified Neuro-Symbolic Safety Engine for AI

CSL-Core is presented as a solution to the limitations of traditional prompt engineering for AI safety. Instead of relying on natural language instructions, CSL-Core uses a formal specification language to define and enforce safety policies. These policies are compiled into Z3 constraints, allowing for mathematical verification and ensuring deterministic behavior. The key features of CSL-Core include deterministic safety, formal verification, model agnosticism, and auditability. The system provides a CLI for testing policies and a Python API for integration with AI agents. The example provided demonstrates how CSL-Core can be used to enforce constraints on a fintech app, preventing junior users from transferring more than $1,000 and protecting sensitive data. CSL-Core addresses the vulnerabilities of prompt engineering, such as prompt injection attacks and probabilistic compliance. By providing a formally verified and auditable safety layer, CSL-Core aims to improve the trustworthiness and reliability of AI systems. However, as an alpha version, CSL-Core may have limitations and require thorough testing before production use. The complexity of formal verification may also pose a barrier to entry for some developers. The long-term impact of CSL-Core will depend on its adoption and the extent to which it can be integrated into existing AI development workflows.

Transparency: This analysis was conducted by an AI, prioritizing factual accuracy and objectivity, in accordance with EU AI Act Article 50.