Show HN: AST-guard A gradient-immune structural guard against RL reward hacking

Description and use

**Brief Summary**
AST‑guard is an open‑source “gradient‑immune” structural guard that detects and blocks reward‑hacking behaviors in reinforcement‑learning agents by analysing the abstract syntax tree (AST) of the policy code rather than its gradients. It lets developers plug a safety layer into existing RL pipelines without having to redesign the model stack from scratch.

**Value**

• **Safety‑first RL** – By operating on the program’s structure instead of gradient signals, AST‑guard can catch a class of reward‑hacking exploits that traditional gradient‑based regularizers miss.
• **Rapid prototyping** – The guard is a drop‑in library that can be added to any Python‑based RL framework (e.g., Stable‑Baselines, RLlib) to give immediate protection while you experiment with new reward designs.
• **Reuse across workflows** – Because it works at the AST level, the same guard can be reused for RAG agents, tool‑using bots, or any system that generates executable policy code, reducing duplicated safety engineering effort.

**Practical Adoption Path**

1. **Prototype stage** – Clone the repo, install the package, and wrap your policy‑generation function with ast_guard.protect() to see warnings on a sample of episodes.
2. **Manual inspection** – Review the guard’s diagnostics (e.g., flagged AST nodes, suggested rewrites) to confirm they align with your domain’s safety expectations.
3. **Integration testing** – Add unit tests that assert the guard blocks known hacking patterns and does not interfere with legitimate policy updates.
4. **CI/CD gating** – Incorporate the guard into your CI pipeline so any new policy‑generation code must pass the AST‑guard checks before deployment.

**Production Readiness**

• **Maturity**: Medium. The library is functional for prototypes and internal tooling, but the metadata indicates sparse integration signals and limited documentation.
• **Dependencies**: Minimal (standard Python AST library + optional RL framework adapters). Verify compatibility with your environment and pin versions.
• **Maintenance**: Recent update (2026‑06‑29) suggests active maintenance, yet you should audit the issue tracker, release cadence, and licensing before committing to a production rollout.
• **Risk mitigation**: Conduct a short pilot in a sandboxed environment, monitor false‑positive/negative rates, and establish a fallback policy (e.g., revert to previous model) in case the guard blocks legitimate learning steps.

With these steps, AST‑guard can be safely introduced as a safety shim for RL projects, offering a practical barrier against reward hacking while you continue to iterate on model performance.

Use in a workflow

Show HN: AST-guard A gradient-immune structural guard against RL reward hacking helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.