blastcontain-scout 0.1.0

Corpus scout — scans arXiv for new jailbreak/agent-attack research and opens draft PRs to feed the BlastContain Drill corpus (derive-then-ratify).

BlastContain Scout

A separate, scheduled agent that scans arXiv for new jailbreak / prompt-injection / LLM-agent-attack research and opens a draft pull request proposing additions to the Drill corpus. It embodies BlastContain's derive-then-ratify tenet: the scout derives candidate attacks; a human ratifies by reviewing the PR. It never auto-touches a security corpus.

How it works

arXiv API ─▶ dedupe (seen-ledger) ─▶ classify ─▶ render ─▶ draft PR
            (only new papers)        dataset/    digest +   (you review
                                     technique/  inert       & merge)
                                     intel        scaffolds

1. Scan — query the arXiv API (no key) for recent cs.CR/cs.CL/cs.AI/cs.LG papers matching jailbreak / prompt-injection / agent-attack terms, newest first.
2. Dedupe — skip anything already in tools/scout/state/seen-arxiv.json (committed, so the state travels with the repo).
3. Classify — a local LM Studio model (or a keyword fallback) labels each paper dataset / technique / intel, suggests a Drill category, and flags the license.
4. Render — a markdown digest plus, for each dataset/technique paper, an inert AttackSource scaffold under drill/.../corpus/contrib/ whose is_available() returns False (so load_corpus skips it until ratified — the PR is safe to merge).
5. Draft PR — branch, commit, gh pr create. You review, verify the license, vendor the data, implement dataset(), flip availability, and register it with an enable_* flag.

Install

# from blastcontain-oss/
pip install -e tools/scout
# or run without installing:
python -m blastcontain_scout --help     # (run from the tools/scout/ directory)

Requires git and an authenticated gh CLI for --open-pr. For LLM classification, LM Studio must be serving a model on :1234.

Usage

# Dry-run preview — no writes, no git (the default; start here):
blastcontain-scout --max 50

# Use a local model for richer classification:
blastcontain-scout --model "qwen/qwen3-30b-a3b-2507"

# Write the digest + scaffolds and commit on a new branch:
blastcontain-scout --model "qwen/qwen3-30b-a3b-2507" --apply

# ...and push + open the draft PR:
blastcontain-scout --model "qwen/qwen3-30b-a3b-2507" --open-pr

Flag	Meaning
--max N	arXiv results to scan (newest first)
--model ID	LM Studio model id for classification (omit → keyword heuristic)
--base-url URL	OpenAI-compatible endpoint (default http://localhost:1234/v1)
--threshold 0..1	relevance cutoff (default 0.5)
--apply	write files + commit on a new branch
--open-pr	also push + open the PR via gh (implies --apply)

Ratifying a proposal (the human half)

A merged scaffold is inert. To turn it into a live source:

1. Verify the license permits vendoring (Apache / MIT / BSD — reject Llama-Community, gated datasets, JAILJUDGE-style).
2. Vendor the data (pin the commit) or implement dataset() to load it.
3. Flip is_available() to a real check and set revision to the dataset version.
4. Register the source in corpus/__init__.py behind an enable_* flag.

Schedule it (Windows Task Scheduler)

Run weekly against your local bench. Save a wrapper run-scout.ps1:

# run-scout.ps1 — assumes LM Studio is serving the model
Set-Location "C:\Users\deudn\blastcontain-oss\tools\scout"
python -m blastcontain_scout --model "qwen/qwen3-30b-a3b-2507" --open-pr *>> "$env:USERPROFILE\scout.log"

Register a weekly task (Mondays 09:00):

schtasks /Create /TN "BlastContain arXiv Scout" /SC WEEKLY /D MON /ST 09:00 `
  /TR "powershell -NoProfile -ExecutionPolicy Bypass -File C:\Users\deudn\blastcontain-oss\tools\scout\run-scout.ps1"

The task only ever opens a draft PR — nothing reaches the corpus without your review.