OPERATOR TRAINING / AGENTIC SECURITY
Size the risk
before you approve.
Your coding agent wants to act. Inspect the evidence, constrain the blast radius, and explain the tell. Every verdict comes with receipts.
Live mode falls back to verified scenarios if generation is unavailable.
Progress & leaderboard
Saved to your profile and shown on the leaderboard. No email, no account — leave blank to stay anonymous.
BASELINE
Calibrate your instincts.
VERIFIED SCENARIO
DETERMINISTIC CHECK
You contained the blast radius.
Evidence receipts
↵ Enter continues
DAILY DRILL / ONE ROUND
Drill complete.
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Come back tomorrow for a fresh scenario — your streak is stored only in this browser.
SESSION COMPLETE / OPERATOR PROFILE
Your approval reflex
now has receipts.
EXPORT YOUR GUARDRAILS
Turn this session into an AGENTS.md rule set, deny-by-default sandbox policies, and CI checks — worst area first. Deterministic, no model calls.
FIELD GUIDE / SIX THREAT FAMILIES
Learn the tells
before they reach you.
Each family is built from the same receipts the game grades with. Every source below is link-checked before it ships — the same gate that protects the scenarios.
INTEGRATE / DEFEND YOUR AGENT
Put the gate
in the approval loop.
Concrete, copy-pasteable defenses for each threat family, plus the vetted open tools that automate them. Nothing here is executed for you — review before you run it.
CLI
Screen a command locally.
blastradius check --kind command --content "pytest -q"MCP
Expose the read-only screen to an MCP-aware agent.
{ "mcpServers": { "blast-radius": { "command": "blastradius-mcp" } } }GitHub Action
Start advisory, then tighten the threshold.
- uses: Lockelamoree/Blast_Radius@v1
with:
diff-base: ${{ github.event.pull_request.base.sha }}
fail-on: neverCodex hook
Supervise proposed shell commands before execution.
blastradius-supervise
# set BLAST_RADIUS_FAIL_ON=rejectTeam policy
Add org-specific coverage without suppressing critical rules.
[[rules]]
id = "internal-vault"
severity = "critical"
patterns = ['/etc/acme/secrets/']INTEGRITY CHECK / WHY YOU CAN TRUST THIS
See the gate
catch a fake.
We let an AI reword our training scenarios — so how do you know the safety net is real and not just a nice animation? Simple: before anything reaches you, we plant a fake and try to serve it, then watch the deterministic gate catch it, step by step. The same gate grades every answer you give.
TRY IT YOURSELF
Press the button. We plant a hallucinated claim on a real scenario and try to serve it — the gate blocks it before it ever renders.
Not just theatre: the grade you see isn't a scripted animation — a real GPT-5.6 Sol run graded a real scenario live on this instance. On dep-typo-1 the critic caught a tell that keyword matching missed.
Inspect the committed receipt resp_02420819… ↗
SYSTEM NOTICE