Enforce security filters using static code analysis, runtime vulnerability detection, reinforcement learning with security rewards, and fine-tuned secure coding datasets.
Here is the code snippet you can refer to:

In the above code we are using the following key approaches:
- Static Analysis (Bandit Security Check):
- Uses AST-based security checks to detect unsafe code.
- Multiple Code Samples with Filtering:
- Generates several completions, selecting the most secure one.
- Reinforcement Learning with Security Rewards (Optional Enhancement):
- Fine-tunes models to reward secure patterns while penalizing vulnerabilities.
- Fine-Tuned Secure Coding Dataset:
- Trains on secure code corpora to minimize risky generations.
Hence, by integrating static code analysis, security-aware filtering, RL-based secure coding, and fine-tuned datasets, code completion models can generate safe, vulnerability-free code snippets while preventing insecure outputs.