dspy-program-security

Secure DSPy prompt programs by enforcing per-module tool allowlists and strict typed outputs. Reviewing module signatures, adapters, and prompt-program composition ensures isolation and limits the surface area for tool wrapper security risks.

Insecure optimizer datasets risk poisoning compiled prompts and deployed artifacts with unsafe content. Establishing a dataset curation pipeline prevents insecure optimizer data from propagating data leakage or injected content into production DSPy programs.

Enforcing strict typed outputs for every DSPy module signature is required to limit the surface area for risk. Typed outputs work alongside per-module tool allowlists to enforce isolation and safe tool usage during development and review.

The best way to review DSPy compiled programs before deployment is a structured compiled-program review process. Inspecting compiled prompts, optimizer datasets, and tool wrappers detects injected content, misconfigurations, and boundary violations between prompt programs and tooling.

DSPy compiled prompts contain unsafe content when optimizer datasets are not properly curated. If optimizer datasets or tool wrappers contain unsafe content, security gaps propagate into production prompts, requiring a dataset curation pipeline to prevent data leakage.

You can prevent boundary violations between DSPy modules and tooling by reviewing module signatures and prompt-program composition. This approach ensures isolation and safe tool usage by enforcing per-module tool allowlists and strict typed outputs across datasets and compiled artifacts.