Hey everyone! I've been digging into the SAFECode "Practical Security Stories and Security Tasks for Agile Development" paper, trying to see how it could map onto our local OpenClaw setups. It's all about weaving security into the dev lifecycle with user stories and concrete tasks, which feels like a great fit for avoiding the "security as an afterthought" trap I keep falling into 😅

Most of the examples in the paper are for traditional web apps, so I'm trying to translate. For instance, one of their core stories is "As a developer, I want to ensure that user-supplied data cannot be interpreted as code by the system." For us, that's not just about web forms—it's **prompt injection** against the LLM, **malicious model files** in Hugging Face, or even unsafe instructions in a Docker Compose build context. The "security task" would be to implement input validation and sanitization for prompts and to verify model hashes.

Here's a super basic example of how I'm starting to think about it for my `docker-compose.yml`:

```yaml
# Security Task: Isolate the inference service from host and other services
services:
openclaw-nano:
image: openclaw/nano_claw:latest
container_name: nano-inference
# Use a read-only root filesystem where possible
read_only: true
# Limit capabilities
cap_drop:
- ALL
networks:
- internal-ai-net
# Don't mount the model volume as read-write unless absolutely necessary
volumes:
- ./models:/app/models:ro
```

My question is: has anyone else tried applying this SAFECode story/task framework? I'm particularly stuck on how to write a good "security story" for the **supply chain risk** of pulling down a fine-tuned model from Hugging Face. The paper talks about third-party components, but a model isn't a library. What are the concrete, actionable "tasks" for that in a local deployment context?

I feel like if we could build a small library of these stories and tasks tailored for local AI, it would be a huge help for newcomers like me trying to do things right from the start.

- ella