Your scanners find issues. This finds the way in.

PerspectiveGraph joins what you already run - Trivy, Semgrep, Cloud Custodian, Falco, plus your AWS and Kubernetes state - into one graph of your real environment, and asks a single question of it: can someone get from the internet, through privilege that is too broad, to something worth stealing?

On a pull request it asks that question before the merge: the check goes red only when this change opens a route, and the fix comes back as its own pull request. Open source (Apache 2.0), runs on your infrastructure, collects no telemetry.

Twelve seconds of make demo: what is exploitable now → the ranked routes → one route's kill chain and the fix it generates → whether the scores can be trusted. Sample scanner output and seeded verdicts, not a real environment.

• See it running - the same dashboard, published read-only. Nothing to install.
• Check your own AWS account - one read-only command, no deployment.
• Put it on your pull requests - ten lines of YAML.

A score here is what the model concludes from the evidence it was given, not a measured frequency: nothing has been calibrated against field data yet, and the engine says so itself rather than rounding up. What is measured, and what is not.

Check your own account in 30 seconds

No deployment, no Docker, nothing ingested. One static binary asks AWS's own policy evaluator which of your roles can reach administrator - applying the service control policies, permission boundaries and condition keys that a policy reader on its own does not see:

# macOS (Apple silicon); swap darwin_arm64 for linux_amd64, linux_arm64 or darwin_amd64
curl -sSL https://github.com/luiacuaniello/perspectivegraph/releases/latest/download/perspectivegraph_darwin_arm64.tar.gz | tar xz
./perspectivegraph redteam -roles -region eu-west-1

It is read-only and free: each check is one iam:SimulatePrincipalPolicy dry run, which creates nothing and costs nothing, and the only permissions it needs are that and iam:ListRoles, both inside SecurityAudit. Windows builds are on the releases page; every binary is signed with cosign and carries SLSA provenance, and two commands verify both before you run anything.

Add -compare and it also runs the engine over the same account, exiting non-zero where the two disagree. That is how the engine's first real false positive was found, and how it stays fixed. If it disagrees on yours, report it: no report is more useful. From here, how to evaluate this walks to a verdict on your own estate in stages that each end in an answer.

See the whole engine in 90 seconds

make demo

Pulls the published, cosign-signed images, feeds them sample Trivy / Semgrep / Custodian / Falco / Kubernetes / IAM / SSO output, and prints the top attack path with its generated fix. Dashboard on http://localhost:3000, make down to tear it down. It needs Docker, jq and curl, compiles nothing, and takes about 23 seconds from an empty image cache; make demo-build is the same demo built from your working tree.

On Kubernetes, the chart is an official package on Artifact Hub:

helm install perspectivegraph oci://ghcr.io/luiacuaniello/charts/perspectivegraph \
  --version 1.25.1 # x-release-please-version

The chart and the three images it runs (ghcr.io/luiacuaniello/perspectivegraph, -dashboard and -postgres) are signed with cosign keyless and carry an SPDX SBOM and SLSA provenance: verify them rather than taking the supply chain on trust.

The dashboard opens on the decision, not the inventory: what is being exploited now, the fewest changes that remove the most risk, and how far the numbers can be trusted. Routes are ranked by triage priority - what the route reaches, whether runtime confirmed it, how exposed the entry is - so a lower-scoring route can outrank a higher-scoring one.

Why the route is P1, one probability with its range, and every hop with what it lets the attacker do and where its probability came from.	Whether the engine's own scores held up against recorded outcomes - and whether there are enough of them to say.

The screenshots are make demo with seeded verdicts, which is why the calibration panel gives one ("underconfident": across 14 outcomes generated to exercise it, the engine predicted 60% where 71% held up). A fresh install and the public demo report insufficient data instead, until real outcomes exist. The public demo runs on one free VM, so treat it as best-effort.

Why?

A scanner reports that a container carries a critical CVE. It cannot report that the container sits behind an internet-facing load balancer, runs with a role that reads the production database, and is therefore the one finding out of ten thousand worth fixing this week. That needs the other tools' output in the same graph, which is what this builds. A developer gets a check that goes red only when their change opens a real route; a security team gets a short ranked list of attack paths instead of a flat list of findings.

Block the pull request that opens the path

No deployment required. The runner reads your estate read-only, ingests this pull request's scan, and answers in-process with the same engine:

- uses: luiacuaniello/perspectivegraph@v1
  with:
    mode: local
    aws-region: eu-west-1     # read-only; give the job an OIDC role with SecurityAudit
    report: trivy.json

The check goes red when this change opens a route to a sensitive asset - or makes one likelier - not when it adds a critical CVE: a critical on a host nothing routes to does not fail the build, and a medium on a container that now reaches the production database does. Routes that were there before the change do not count, even when they run through what it touches: the scan is applied to a copy of the estate and compared with it, and nothing is written. It also has a third outcome, because a pipeline whose scan never arrived must not get the same green tick as one that is clean:

Outside GitHub Actions it is one command, and it installs as a Trivy plugin too:

perspectivegraph gate -local -aws-region eu-west-1 -report trivy.json -slug owner/name -sha "$COMMIT_SHA"

trivy plugin install github.com/luiacuaniello/perspectivegraph
trivy image -f json myapp:pr-42 | trivy perspectivegraph gate -local -aws-region eu-west-1 -report -

[!WARNING] Fork pull requests get no secrets, so the gate fails closed as unknown on them. Do not work around it with pull_request_target: that runs your secrets - in local mode, cloud credentials - against the contributor's code.

On a public repository, a blocked check prints the route (real asset names, the CVE, the sensitive asset) into a public job log. Use soft-fail and post the detail somewhere private.

The manual covers the rest: pointing the action at a deployed engine, gating rendered manifests, a pre-collected estate, rolling the gate out, and every input in action.yml.

Let an agent query it

A language model cannot enumerate thousands of edges reliably or run Dijkstra, and asked for "the attack paths in my account" it will invent plausible ones. So the engine speaks MCP: the agent asks, and reasons over answers it could not have made up.

perspectivegraph mcp --api http://localhost:8080   # or https://demo.a3thinker.it, no credential needed