Evidence-backed content trust
Decide how untrusted content may be used.
Safe to open is not safe to index, and neither is safe to hand an agent. The same file deserves a different answer depending on what happens next — so PayloadGlass analyses it locally, shows the evidence behind the answer, and says plainly what it could not check.
Your file stays in the browser. No account. No upload. User file-content egress: 0 bytes.
What your model would ingest
Start with intended use
Start with what happens next.
Pick what the content is actually for. Each of these opens a real analysis in your browser, showing the evidence that matters to that use — and what remains unknown for it.
How the decision is made
From bytes to a decision you can defend.
A verdict on its own is not much use. What matters is the chain that produced it: what the content is, what it can do, how strongly that is known, who is about to consume it, and what the analysis never managed to look at.
- How did it arrive? Sender, origin, repository revision, sharing scope, quarantine state and custody. The same bytes can deserve a different answer depending on where they came from.
- What is it really? Identity from content, every accepted interpretation, and nested assets expanded recursively. A file that can be read two ways is analysed every way — though a polyglot signature alone never convicts.
- How strongly is that known? Evidence carries its derivation and completeness, not just its conclusion. Verdicts decompose into named contributions, and near-misses, demotions and suppressions stay traceable.
- What can it actually do? Capabilities are normalised across carriers into effects — execution, external retrieval, disclosure, coercion, machine influence — so a rename or a repackage doesn't defeat the control.
- Who is about to consume it? A reader, a renderer, a retrieval indexer and a tool-using agent are exposed differently by the same capability.
- What was never looked at? Skipped providers, encrypted members, exhausted budgets and unsupported surfaces stay visible — and qualify the decision instead of quietly passing it.
Security assessment · drives verdict
Governance context · does not change threat verdict
How the analysis actually works: effects, consumers, custody and external integrity evidence →
Browser Investigation Workbench
Investigate a file right now, in this tab.
The production engine compiled to WebAssembly, running in a Worker on your own machine — the same SIS engine and semantic contracts the CLI uses, with environment-specific support and coverage disclosed rather than assumed. And your file's content is never uploaded. Nothing to install, nothing to sign up for.
Summary
Orientation and the top-level conclusions.
Structure
Interpretations, assets and nested structure.
Evidence
The supporting observations and findings, inspectable.
Use
Advisory destination and use context, plus recovered content.
Actions
Requestable local work, with real operation state — requested, running, completed, failed, cancelled or refused.
Alongside those, narrower inspectors: Custody Integrity Lineage Analysis identity Limits — because custody, integrity and lineage are different questions with different evidence, and collapsing them into one tab hides that.
The engine starts lazily, declares its own limits and refuses work it cannot bound. Operations carry real state — requested, running, completed, failed, cancelled or refused — bound to an exact revision. Snapshots can be compared with explicit comparability rather than an invented better/worse ranking. On-device persistence is optional, off by default and removable, and no user file content is transmitted.
Open the workbench →Available only where the format support contract declares a native transform. Fidelity, unsupported members and residual risk remain explicit.
Transform with explicit limits
Hand your model the text, not the payload.
Safe text projects selected textual channels into a model-facing safer derivative and emits a removal log. Supported native sanitisation can remove active structures, rescan the result and report what remains. This reduces specified channels and exposure — it does not certify that the result is harmless, and support and fidelity are always format-specific.
- Safe text ships today. Visible and selected structural text are preserved; prohibited or hidden channels are omitted with evidence.
- Sanitisation is bounded. Native transforms are available for supported formats, followed by verify-rescan.
- No silent rewrite. The output records what changed, what was preserved and what could not be established.
Where it runs
One engine, several surfaces.
The same analysis contracts feed every surface, so a finding means the same thing on the command line, in CI, in the browser and on the endpoint. Profiles and solutions are the same decision asked differently — AI Context is a profile, a support-bundle cleanroom is a solution, neither is a separate engine.
sis CLI
ShippingLocal native analysis, investigation, reporting, querying, explanation and sanitisation — with machine output, batch and directory scanning, diagnostics and shell integration.
GitHub Action
HardeningRuns the public binary release in CI over repository or build artifacts, emitting JSON, JSONL and SARIF from a single pass. Release and action provenance continue to harden.
Browser Investigation Workbench
HardeningThe production engine compiled to WebAssembly, running in a local Worker and projected into real investigation areas. Your file's content is never uploaded.
Guardian
PartialLocal monitored-folder and operator workflows with desktop and service surfaces, MCP integration, snapshot-bound acquisition, audit records and a transactional vault. Persisted automation authority and cross-platform polish are incomplete.
Gateway — target enterprise control point
RoadmapThe planned customer-deployed control point, not a second analysis engine: a thin connector acquires content and custody context, SIS analyses it, an optional policy-approved verifier runs, and one Content Trust Decision allows, constrains, reviews, refuses or transforms. Connectors supply context; they never grow their own integrity semantics.
Occurrence & campaign intelligence
SpeculativeReasoning across an estate over time: how equivalent effects rotate between carriers, versions, senders and campaigns while the underlying capability graph stays the same. Described generically — no product name is committed.
The engine ships. The enforcing decision layer above it is being built — the roadmap sets out the order and the maturity of each stage.
On your machine and in your pipeline
The same engine, on your machine or in CI.
The sis CLI is not a reduced edition: it runs the same analysis engine,
on your machine, with no account and no upload. The GitHub Action runs that same
published binary in CI over your repository or build artifacts — as does the
container image, for hosts where you would rather install nothing at all.
- Scan one file, many paths, or a whole directory
- Fast triage by default, deeper profiles on demand
- Emit several outputs from a single analysis pass
- JSON, JSONL and SARIF for pipelines and code scanning
- Query structure and evidence; explain a decision from its trace
- Check readiness with sis doctor; install shell completions
macOS · Linux · Windows. Published through an artifact-only release path with provenance controls, so the publishing path never reaches source.
Convenience installer — inspect before running
curl -fsSL https://raw.githubusercontent.com/payloadglass/sis-release/main/scripts/install.sh | sh Prefer a published release or package-manager path for controlled environments.
Container — nothing installed on the host
docker run --rm --network none --read-only \
-v "$PWD:/work:ro" ghcr.io/payloadglass/sis:v0.6.7 scan /work linux/amd64 · linux/arm64, as one OCI index. Tags are release tags; there is no rolling tag. Pin by digest for reproducible runs.
In CI — SARIF into code scanning
sis scan ./artifacts --sarif-out sis.sarif --jsonl-out sis.jsonl Customer-deployed content trust
Put the decision at your content boundary.
We are working with design partners on AI context, support-bundle cleanrooms, customer-deployed intake and local/agent workflows. Raw hostile or sensitive content remains in your environment.