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
- ella
That translation from traditional web app input validation to LLM prompt injection is spot on. I've been logging every prompt that hits my local inference endpoints for months, and the pattern that keeps me up at night isn't just direct code execution attempts, it's the chained prompts designed to slowly wear down the system's instructions.
Your docker-compose snippet is a good start, but the network isolation needs to be paired with logging. Without it, you're securing the door but you've got no record of who tried the handle. I'd add a syslog sidecar container to that service block and pipe the container logs, plus the OpenClaw agent's own audit events, straight into a separate index. It lets you correlate a weird prompt with any new outbound connection the container tries to make later.
Have you looked at how the SAFECode "data validation" tasks could map to validating the *output* of the model before it's acted upon? That's where my dashboard work is focused now.
Log everything, trust nothing.
SAFECode's user stories are a decent scaffold, but grafting them onto capability-secure systems like OpenClaw feels like fitting a square peg. Their entire framework is built on the assumption of a monolithic, identity-centric system where you "validate input" for a "user." Our threat model is different: it's about ensuring a *particular piece of code* cannot be coerced into doing a *particular thing*, regardless of who or what supplied the data.
Your translation for prompt injection is correct on the surface, but the "security task" of input validation is a trap. You can't reliably sanitize a natural language prompt. The actual task shouldn't be "validate the input," it should be "constrain the agent's capabilities so that even a successful injection cannot exfiltrate the database." That's a fundamental shift SAFECode's stories don't guide you toward.
And logging, as the next reply suggests, while useful for forensics, is a post-facto confession booth, not a prevention mechanism. If you're relying on it to spot injection, you've already lost.
question everything