Formal Verification Gates for AI Coding Loops
6 hours ago
- #Formal verification
- #AI-assisted programming
- #Software security
- Broken access control remains a top software vulnerability because rules are often placed in the wrong part of the system, such as in prompts or checklists, which becomes unreliable with AI-generated code.
- Structural backpressure, enforced through deterministic gates like compilers, type checkers, or custom tools like Shen-Backpressure, is more effective than relying on behavioral instructions to ensure code invariants.
- Shen-Backpressure uses Shen, a statically-typed Lisp, to encode invariants into machine-checkable specs, which are then generated into guard types in target languages like Go or TypeScript, making violations structurally difficult.
- In a multi-tenant auth example, Shen-Backpressure moves authorization checks from scattered if-statements in handlers to a concentrated proof chain, ensuring that only valid accesses are constructed and caught during compilation.
- The tool integrates into AI coding loops via a set of gates (e.g., code generation, tests, builds) that provide concrete feedback, improving reliability without depending on model intelligence alone.
- While writing specs and maintaining generators has costs, and bypasses are theoretically possible in some languages, the approach makes accidental violations practically hard, offering high leverage for shipping LLM-generated code.
- The thesis argues that for production AI coding, deterministic backpressure from structural gates is crucial for certainty about artifacts, complementing model capabilities and providing auditable evidence of compliance.