Here’s a practical way to make StellaOps truly **offline‑ready** while upgrading provenance and compliance: combine **DSSE signing** with **in‑toto link attestations**, and cache entries in a **local Rekor‑style Merkle log** so builds remain provable even when air‑gapped—nicely aligned with the eIDAS2.0 shift toward Qualified Trust Services. ([GitHub][1])
**Why these pieces**
* **DSSE**: a simple, envelope‑style signature that authenticates payload + type; widely used for attestations (e.g., cosign). Works well in offline flows. ([GitHub][1])
* **in‑toto**: records step‑by‑step build “link” metadata (materials, command, products) you can verify later. v1.0 attestation framework is stable. ([in-toto][2])
* **Rekor‑style transparency log**: append‑only Merkle tree with inclusion/consistency proofs; you can run it privately/offline and shard as it grows. ([Sigstore][3])
* **eIDAS2.0 context**: new implementing acts and timelines tighten expectations on trust services and evidence—your local, verifiable log + signatures help map to those controls. ([Fabasoft][4])
**Minimal design (deterministic, air‑gapped)**
1.**Per‑step capture**
* Wrap each build step with `in-toto-run` to emit **link** attestations (DSSE‑wrapped). Pin inputs/outputs by digest; record exact argv/env. ([in-toto][2])
2.**Deterministic DSSE**
* Generate DSSE envelopes from normalized JSON (no clocks/paths). Sign with offline keys (HSM/PKCS#11 or file keys) and tag with predicate type (SLSA provenance, scan evidence, policy results). ([GitHub][1])
3.**Local transparency log**
* Store every envelope (and SBOM/VEX) in a **local Rekor clone** (same API & Merkle proofs). Enable periodic **sharding** and snapshot the tree head into your evidence bundle. ([Sigstore][5])
4.**Proof bundle**
* For each artifact, export: artifact digest, DSSE envelopes, in‑toto links, Rekor **inclusion + consistency proofs** (tree size, root). Verifiers can check integrity without the network. ([Su3][6])
5.**Online/Offline bridge (optional)**
* When connected, mirror your local tree to a public/partner log; when fully air‑gapped, use a **witness** or transfer pack to sync later. ([Sigstore][7])
* Show a **“Provenance Card”** on each artifact: green checks for DSSE, required links present, Rekor inclusion proof verified, and a “clipboard‑copy” of the tree head for audit packets. (When online, add “mirrored to public log” badge.)
If you want, I can draft the DSSE predicates we’ll use (build, scan, policy), the Rekor‑compatible schema for the local log, and a tiny verifier in C# to validate DSSE + Merkle proofs offline.
[1]: https://github.com/secure-systems-lab/dsse?utm_source=chatgpt.com "DSSE: Dead Simple Signing Envelope"
Here’s a quick heads‑up that saves a *ton* of pain when sorting package versions on RHEL/Fedora/SUSE‑style systems: **never compare RPM versions as plain strings.** RPM compares **EVR** — `Epoch:Version-Release` — left‑to‑right, and if epochs differ, it stops right there. Missing epoch is treated as `0`. Backports (e.g., old Version with higher Release) and vendor epochs will break naive compares. Use an **rpmvercmp‑equivalent** and persist versions as a 3‑tuple `(epoch, version, release)`. ([RPM][1])
**Why this matters**
*`1:1.0-1`**>** `0:2.0-100` because `1` (epoch) beats everything after. ([RPM][1])
* Fedora/Red Hat guidelines explicitly say EVR ordering governs upgrade paths; epochs are the most significant input and shouldn’t be removed once added. ([Fedora Docs][2])
**Correct approach (any language)**
* Parse to **NEVRA** (Name, Epoch, Version, Release, Arch), then compare by **EVR** using rpm’s algorithm; don’t roll your own string logic. ([Docs.rs][3])
* If you can’t link against librpm, use a well‑known **rpmvercmp** implementation for your stack. Python and PHP have ready helpers. ([PyPI][4])
**Drop‑in options**
* **Python**: `rpm-vercmp` (pure Python) for EVR compares. Store `epoch` as int (default `0`), `version`/`release` as strings, and call the comparator. ([PyPI][4])
* **.NET/C#**: no official rpmvercmp, but mirror the spec: split EVR, compare epochs numerically; for `version`/`release`, compare segment‑by‑segment using rpm rules (alphanumeric runs; numeric segments compare as integers; tildes sort before anything, etc.). (Spec summary in rpm‑version(7).) ([RPM][1])
* **Rust/Go**: model NEVRA (existing crates/docs show structure) and wire a comparator consistent with rpmvercmp. ([Docs.rs][3])
**Practical tips for your pipelines**
* **Persist EVR**, not strings like `“1.2.3-4.el9”`. Keep `epoch` explicitly; don’t drop `0`. ([Fedora Docs][2])
* **Normalize inputs** (e.g., from `rpm -q` vs `repoquery`) so missing epochs don’t cause mismatches. ([CPAN][5])
* **Backport‑aware sorting**: rely on EVR, *not* semver. Semver comparisons will misorder distro backports. (Fedora docs highlight EVR as authoritative.) ([Red Hat Docs][6])
If you want, I can sketch a tiny C# `RpmEvrComparer` tailored to your .NET 10 repos and wire it into your SBOM/VEX flows so Feedser/Vexer sort updates correctly.
Here’s a practical way to make your vulnerability signals “point‑in‑time correct,” so a deployment on (say) 2025‑10‑17 is evaluated against exactly what vendors knew on 2025‑10‑17—not today’s retroactive data.
# Why this matters
Vendor feeds change: CVEs get split/merged, severities are re‑scored, ranges are corrected. If you don’t snapshot advisories with dates, your scanner can’t reproduce past results or pass audits.
# Core ideas (simple terms)
* **Immutable, dated snapshots:** store every advisory feed exactly as fetched, tagged by retrieval timestamp.
* **Point‑in‑time resolution:** when you ask “is v1.2.3 affected as of 2025‑10‑17?”, evaluate only the snapshots at or before that date.
* **Version‑aware schemas:** use formats that encode version ranges precisely so queries are deterministic.
# Feeds to mirror (daily or hourly)
* **OSV** (Open Source Vulnerabilities). Great for ecosystem packages; models affected ranges and fixed versions cleanly.
* **Vendor OVAL** (e.g., Red Hat, Debian, Ubuntu, SUSE). Machine‑readable OS advisories with package/build info.
Here’s a compact, plug‑and‑play plan to build a **cross‑distro “golden set”** so your retrieval can correctly handle **backported fixes** and avoid false “still vulnerable” flags.
---
# What this golden set is
A small, curated corpus of tuples **(distro, release, package, CVE)** with:
* the **vendor‑declared fixed version** (what the distro claims)
* a **counterexample** where **upstream is still affected** but the distro **backported** the patch (so version comparison alone would be misleading)
Use it as regression tests + seed facts for your policy engine and matchers.
## Product Advisory: Deterministic VEX-first vulnerability verdicts with CycloneDX 1.7
### 1) The problem you are solving
Modern scanners produce a long list of “components with known CVEs,” but that list is routinely misleading because it ignores *context*: whether the vulnerable code is shipped, configured, reachable, mitigated, or already fixed via backport. Teams then waste time on false positives, duplicate findings, and non-actionable noise.
A **VEX-first** approach solves this by attaching *exploitability/impact assertions* to SBOM components. In CycloneDX, this is expressed via the **Vulnerability / Analysis** model (often used as VEX), which can declare that a component is **not affected**, **under investigation/in triage**, **exploitable/affected**, or **resolved/fixed**, along with rationale/justification and other details. CycloneDX explicitly frames this as “vulnerability exploitability” context, including a `state` and a `justification` for why a vulnerability is (or isn’t) a practical risk. ([cyclonedx.org][1])
The core product challenge is therefore:
* You will ingest **multiple statements** (vendors, distros, internal security, runtime evidence) that may **conflict**.
* Those statements may be **conditional** (only affected on certain OS, feature flags, build options).
* You must produce a **single stable, explainable verdict** per (product, vuln), and do so **deterministically** so audits and diffs are reproducible.
---
### 2) Product intent and outcomes
**Primary outcome:** Reduce noise while increasing trust: every suppression or escalation is backed by evidence and explainable logic.
**What “good” looks like:**
* Fewer alerts, but higher signal.
* Each vuln has a clear **final verdict** plus **reason chain** (“why this was marked not_affected/fixed/affected”).
* Deterministic replay: the same inputs produce the same outputs.
---
### 3) Recommended data contract (CycloneDX 1.7 aligned)
Use CycloneDX 1.7 as the canonical interchange for impact/exploitability assertions:
* **Vulnerability entries** with **analysis** fields:
*`analysis.state` (status in context) and `analysis.justification` (why), as described in CycloneDX’s exploitability use case. ([cyclonedx.org][1])
* Optional ingress from **OpenVEX** or CSAF; normalize into CycloneDX analysis semantics (OpenVEX defines the commonly used status set `not_affected / affected / fixed / under_investigation`, and requires justification in `not_affected` cases). ([GitHub][2])
Graph relationships (if you use SPDX 3.0.1 as your internal graph layer):
* Model dependencies and containment via SPDX `Relationship` and `RelationshipType`, which formalize “Element A RELATIONSHIP Element B” semantics used to compute transitive impact. ([SPDX][3])
---
### 4) Product behavior guidelines
#### A. Single “Risk Verdict” per vuln, backed by evidence
Expose one final verdict per vulnerability at the product level, with an expandable “proof” pane:
* Inputs considered (SBOM nodes, relationship paths, VEX statements, conditions).
* Merge logic explanation (how conflicts were resolved).
* Timestamped lineage: which feed/source asserted what.
#### B. Quiet-by-design UX
* Default views show only items needing action: **Affected/Exploitable**, and **Under Investigation** with age/timeouts.
* “Not affected” and “Fixed/Resolved” are accessible but not front-and-center; they primarily serve audit and trust.
#### C. Diff-aware notifications
Notify only on **meaningful transitions** (e.g., Unknown→Affected, Affected→Fixed), not on every feed refresh.
---
### 5) Development guidelines (deterministic resolver)
#### A. Normalize identifiers first
Create a strict canonical key for matching “the same component” across SBOMs and VEX:
1. prefer **purl**, then **CPE**, then (name, version, supplier).
2. persist alias mappings (vendor naming variance is normal).
#### B. Represent the world as two layers
1.**Graph layer** (what is shipped/depends-on/contains what)
2.**Not affected** (with valid justification and conditions satisfied)
3.**Affected/Exploitable**
4.**Under investigation / In triage**
5.**Unknown**
CycloneDX’s exploitability model explicitly supports “state + justification” to make “not affected” meaningful, not a hand-wave. ([cyclonedx.org][1])
#### E. Propagation rules must be explicit
Decide and document how assertions propagate across the dependency graph:
* When a dependency is **Affected**, does the product become Affected automatically? (Typically yes if the dependency is shipped and used, unless a product-level assertion says otherwise.)
* When a dependency is **Not affected** due to “code removed before shipping,” does the product inherit Not affected? (Often yes, but only if you can prove the affected code path is absent for the shipped artifact.)
If you want, I can also provide a short, implementation-ready “resolver contract” (types, verdict lattice, proof schema) that is CycloneDX 1.7-centric while remaining neutral to whether you store the graph as CycloneDX dependencies or SPDX 3.0.1 relationships.
[1]: https://cyclonedx.org/use-cases/vulnerability-exploitability/?utm_source=chatgpt.com "Security Use Case: Vulnerability Exploitability"
[2]: https://github.com/openvex/spec/blob/main/OPENVEX-SPEC.md?utm_source=chatgpt.com "spec/OPENVEX-SPEC.md at main"
Here’s a simple, high‑signal pattern you can drop into your security product: **gate AI remediation/explanations behind an “Evidence Coverage” badge**—and hide suggestions when coverage is weak.
---
### What this solves (plain English)
AI advice is only trustworthy when it’s grounded in real evidence. If your scan only sees half the picture, AI “fixes” become noise. A visible coverage badge makes this explicit and keeps the UI quiet until you’ve got enough facts.
---
### What “Evidence Coverage” means
Score = % of the verdict’s required facts present, e.g., do we have:
* **Reachability** (is the vulnerable code/path actually callable in this artifact/runtime?)
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