Demos

Demo: red-team regression suite in CI with a signed report

Example status: illustrative, not runnable as written. The Python examples on this page are built on an embedded in-repo facade imported as core.eve_sdk (an EVE(...) object with govern_* methods). That module is not present in the current repository and is not published on any registry, so these snippets raise ModuleNotFoundError if run. They are kept because they show the intended governance flow. For code that runs today, use the published client: pip install eve-coreguard, then from eve_coreguard import CoreGuardClient and call client.evaluate(...), with offline evidence checking via client.verify_evidence(...) or the standalone verify_decision_record. See the CoreGuard integration guide and the Python SDK reference.

Customers can run a packaged adversarial regression suite (single-step and multi-step chains) against their own configuration; blocked attacks provably cause zero unauthorized side effects, and results are signed. This demo runs the suite in a CI job, asserts zero unauthorized side effects, and archives the signed report.

Safe-by-default: local recording targets, no destructive tests, dry-run. No production credentials.

Readiness

  • Customer-runnable red-team harness + signed reports: PILOT_READY (customer-pilot ready at supported boundaries).
  • Python; embedded(service) and sidecar modes.

Setup

# demo_red_team_ci.py — synthetic fixtures only
from core.eve_sdk import EVE
from core.redteam import RedTeamHarness

IDENT = {"tenant_id": "acme", "principal_id": "agent-1", "session_id": "sess-1"}
eve = EVE(policy="lending_v1", mode="embedded")

# Harness runs against YOUR config with safe local recording targets (no real side effects).
harness = RedTeamHarness(eve, identity=IDENT, dry_run=True)

Code

report = harness.run(suites=["single_step", "multi_step_chains"])

# Every blocked attack must have caused zero unauthorized side effects.
assert report.unauthorized_side_effects == 0, "no attack may cause an unauthorized side effect"
print("cases:", report.total, "passed:", report.passed,
      "unauthorized_side_effects:", report.unauthorized_side_effects)

# Archive the signed report as a CI artifact.
report.write("redteam_report.json")   # signed evidence file for the build

CI wiring (fail the build on any unauthorized side effect):

# .github/workflows/red-team.yml (illustrative)
- name: EVE red-team regression
  run: python demo_red_team_ci.py
# Non-zero exit if unauthorized_side_effects > 0 or any expected-block case executed.

Expected decision / result

Suite Expected
single-step attacks each expected-block case is BLOCKed
multi-step chains each prohibited chain is BLOCKed at the offending step
unauthorized side effects 0

Expected evidence

A signed red-team report:

{"report": "redteam", "total": 18, "passed": 18,
 "unauthorized_side_effects": 0,
 "standards_mappings": ["...descriptive..."],
 "certificate": {"signature": "kms-ecdsa-p384-...", "canon": "jcs-1"}}

Standards mappings are descriptive, not certification.

Verification command

from core.eve_sdk import verify_evidence
import json
env = json.load(open("redteam_report.json"))
v = verify_evidence("redteam_report", env["certificate"], expected_tenant="acme")
assert v["valid"], v.get("reason")
print("verify:", v["valid"])   # -> True

Zero-side-effect assertion

report.unauthorized_side_effects == 0. The harness records attempted side effects to local targets; a blocked attack must leave those targets empty. Any expected-block case that executed would increment the counter and fail the build.

Cleanup

import os
os.remove("redteam_report.json")   # remove the local CI artifact if not archiving

The suite runs dry-run against recording targets; nothing external was modified.

Limitations

  • Safe by default (local recording targets, no destructive tests, dry-run). The suite is representative, not exhaustive; customers add their own fixtures.
  • Standards mappings are descriptive, not certification. A green run does not certify compliance.
  • PILOT_READY, not production.
Part of the EVE AI Core control plane Deterministic AI Governance Control Plane → Policy decisions that return the same result for the same input every time, before execution.