add advisories
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StellaOps Bot
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Here’s a compact blueprint for two high‑impact Stella Ops features that cut noise and speed triage: a **smart‑diff scanner** and a **call‑stack analyzer**.
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# Smart‑diff scanner (rescore only what changed)
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**Goal:** When an image/app updates, recompute risk only for deltas—packages, SBOM layers, and changed functions—then attach machine‑verifiable evidence.
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**Why it helps (plain English):**
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* Most “new” alerts are repeats. Diffing old vs new narrows work to just what changed.
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* If a vulnerable API disappears, auto‑draft a VEX “not affected” (NA) with proof.
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* Evidence (DSSE attestations + links) makes audits fast and deterministic.
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**Inputs to diff:**
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* Package lock/manifest (e.g., `package-lock.json`, `Pipfile.lock`, `go.sum`, `pom.xml`, `packages.lock.json`).
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* Image layer SBOMs (CycloneDX/SPDX per layer).
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* Function‑level CFG summaries (per language; see below).
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**Core flow (pseudocode):**
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```pseudo
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prev = load_snapshot(t-1) // lockfiles + layer SBOM + CFG index + reachability cache
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curr = load_snapshot(t)
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Δ.pkg = diff_packages(prev.lock, curr.lock) // added/removed/changed packages
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Δ.layers= diff_layers(prev.sbom, curr.sbom) // image files, licenses, hashes
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Δ.funcs = diff_cfg(prev.cfgIndex, curr.cfgIndex) // added/removed/changed functions
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scope = union(
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impact_of(Δ.pkg.changed),
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impact_of_files(Δ.layers.changed),
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reachability_of(Δ.funcs.changed)
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)
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for f in scope.functions:
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rescore(f) // recompute reachability, version bounds, EPSS, KEV, exploit hints
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for v in impacted_vulns(scope):
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annotate(v, patch_delta(Δ)) // symbols added/removed/changed
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link_evidence(v, dsse_attestation(), proof_links())
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for v in previously_flagged where vulnerable_apis_now_absent(v, curr):
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emit_vex_candidate(v, status="not_affected", rationale="API not present", evidence=proof_links())
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```
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**Evidence & provenance:**
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* Emit **DSSE** envelopes for: (a) diff result, (b) rescoring inputs, (c) VEX candidates.
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* Attach **proof links**: Rekor entry, content digests, source commit, layer digest, and normalized lockfile hash.
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* Deterministic IDs: `sha256(canonical-json(record))`.
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**Data model (minimal):**
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* `Delta.Packages { added[], removed[], changed[{name, fromVer, toVer}] }`
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* `Delta.Layers { changed[{path, fromHash, toHash, licenseDelta}] }`
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* `Delta.Functions { added[], removed[], changed[{symbol, file, signatureHashFrom, signatureHashTo}] }`
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* `PatchDelta { addedSymbols[], removedSymbols[], changedSignatures[] }`
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**.NET 10 implementation hints:**
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* Projects: `StellaOps.Scanner.Diff`, `StellaOps.Scanner.Rescore`, `StellaOps.Evidence`.
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* Use `System.Formats.Asn1`/`System.Security.Cryptography` for digests & signing adapters.
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* Keep a **content‑addressed cache** by `(artifactDigest, toolVersion)` to make rescoring O(Δ).
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**Language normalizers (lockfiles → canonical):**
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* Node: parse `package-lock.json` v2/v3 → `{name, version, resolved, integrity}`.
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* Python: consolidate `pip freeze` + `pipdeptree` or `poetry.lock` into name/version/source.
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* Java: `mvn -DskipTests -q help:effective-pom` + `dependency:tree -DoutputType=json`.
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* Go: parse `go.sum` + `go list -m -json all`.
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* .NET: `dotnet list package --format json` + `packages.lock.json`.
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---
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# Call‑stack analyzer (fast reachability + readable explainers)
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**Goal:** Rank vulns by whether your code can realistically hit the vulnerable sink, and show a **minimal, human‑readable path** (“why here?”).
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**Strategy: hybrid analysis**
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* **Static pre‑compute:** Build language‑specific call graphs (normalize package symbols, collapse known framework boilerplate). Techniques: CHA (Class Hierarchy Analysis), RTA (Rapid Type Analysis), and Spark‑style dataflow over edges.
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* **JIT refinement:** On demand, prune with types/points‑to from build artifacts (PDBs, `dotnet build` metadata, `javac -h`, `tsc --declaration`), eliminate dead generics, inline trivial wrappers.
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* **Path collapse:** Merge equivalent prefixes/suffixes; cap frames to the **smallest user‑code slice** plus critical sink frames.
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**Scoring & ranking:**
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* `score = user_code_distance^-1 * sink_criticality * evidence_weight`
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* `user_code_distance`: hops from repo code to sink (shorter = riskier).
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* `sink_criticality`: CWE/AV:N + KEV/EPSS boost.
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* `evidence_weight`: runtime hints (observed stack traces, symbols present).
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**Explainer format (what triage sees):**
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```
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[Reachable: HIGH] CVE-2024-XXXX in log4j-core@2.14.0
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why here? MyService.Process() → LoggingUtil.Wrap() → org.apache...JndiLookup.lookup()
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minimal path (3/17 frames), pruned 14 library frames
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proof: layer sha256:…, PDB match, symbol hash match, DSSE att#… (click to expand)
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```
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**.NET 10 building blocks:**
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* Build symbol index from PDBs (`Microsoft.DiaSymReader`), Roslyn analyzers for method refs.
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* Generate a compact call graph (`StellaOps.Reach.Graph`) with node IDs = `sha256(normalized-signature)`.
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* JIT refinement: read IL (`System.Reflection.Metadata`) to resolve virtual dispatch edges when type sets are small (from compile artifacts).
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* Renderer: keep to ≤5 frames by default; toggle “show hidden frames”.
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**CFG + function diff for rescoring (bridge to smart‑diff):**
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* Store per‑function signature hash and basic‑block count.
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* On change, register function for rescoring reachability + sinks affecting that symbol.
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---
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# Minimal deliverables to get moving (1 sprint)
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1. **Delta core**: canonical lockfile/Layer/Symbol diff + patch‑delta annotator.
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2. **Rescore loop**: take `Delta` → select functions → recompute reachability & risk.
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3. **Explainer renderer**: minimal‑frames call path with “why here?” badges.
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4. **Evidence emitter**: DSSE envelopes + proof links; VEX NA when vulnerable APIs vanish.
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5. **Cache & determinism**: content‑addressed store; stable JSON; golden tests.
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If you want, I can generate the .NET 10 project skeletons (`StellaOps.Scanner.Diff`, `StellaOps.Reach.Graph`, `StellaOps.Evidence`) and stub methods next.
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Stella Ops’ big advantage is that it treats security findings as **versioned, provable changes in your system** (not a perpetual firehose of “still vulnerable” alerts). That unlocks a bunch of practical wins:
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## 1) Massive noise reduction via “delta-first” security
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Most scanners re-report the whole universe on every build. Stella Ops flips it: **only rescore what changed** (packages, image layers, symbols/functions), and inherit prior conclusions for everything else.
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What you get:
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* Fewer duplicate tickets (“same CVE, same component, nothing changed”)
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* Less rescanning cost and faster CI feedback
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* A clear answer to “what’s new and why?”
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Why this is a real moat: making incremental results *reliable* requires stable canonicalization, caching, and evidence that the diff is correct—most tools stop at “diff packages,” not “diff exploitability.”
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## 2) Reachability-driven prioritization (the call-stack explainer)
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Instead of ranking by CVSS alone, Stella Ops asks: **can our code actually hit the vulnerable sink?** Then it shows the *minimal* path that makes it believable.
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What you get:
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* Engineers fix what’s *actually* dangerous first
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* Security can justify prioritization with a “why here?” trace
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* “Unreachable” findings become low-touch (auto-suppress with expiry, or mark as NA with evidence)
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This is the difference between “we have log4j somewhere” and “this service calls JndiLookup from a request path.”
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## 3) Evidence-first security: every decision is auditable
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Stella Ops can attach cryptographic, machine-verifiable evidence to each conclusion:
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* **Diff attestations**: what changed between artifact A and B
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* **Rescore attestations**: inputs used to decide “reachable/not reachable”
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* **VEX candidates**: “not affected” or “affected” claims with rationale
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A clean way to package this is **DSSE envelopes** (a standard signing wrapper used by supply-chain tooling). DSSE is widely used in attestations and supported in supply chain ecosystems like in-toto and sigstore/cosign. ([GitHub][1])
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What you get:
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* Audit-ready trails (“show me why you marked this NA”)
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* Tamper-evident compliance artifacts
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* Less “trust me” and more “verify me”
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## 4) Auto-VEX that’s grounded in reality (and standards)
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When a vulnerability is present in a dependency but **not exploitable in your context**, you want a VEX “not affected” statement *with a justification*—not an ad-hoc spreadsheet.
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CISA has documented minimum elements for VEX documents, and points out multiple formats (including CycloneDX/OpenVEX/CSAF) that can carry VEX data. ([CISA][2])
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CycloneDX specifically positions VEX as context-focused exploitability information (“can it actually be exploited here?”). ([cyclonedx.org][3])
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What you get:
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* Fast, standardized “NA” responses with a paper trail
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* Cleaner vendor/customer conversations (“here’s our VEX, here’s why”)
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* Less time arguing about theoretical vs practical exposure
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## 5) Faster blast-radius answers when a 0‑day drops
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The “smart diff + symbol index + call paths” combo turns incident questions from days to minutes:
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* “Which services contain the vulnerable function/symbol?”
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* “Which ones have a reachable path from exposed entrypoints?”
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* “Which builds/images introduced it, and when?”
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That’s an *Ops* superpower: you can scope impact precisely, patch the right places, and avoid mass-panic upgrades that break production for no gain.
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## 6) Lower total cost: fewer cycles, less compute, fewer human interrupts
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Even without quoting numbers, the direction is obvious:
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* Delta rescoring reduces CPU/time and storage churn
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* Reachability reduces triage load (fewer high-severity false alarms)
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* Evidence reduces audit and exception-management overhead
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Net effect: security becomes a **steady pipeline** instead of a periodic “CVE storm.”
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## 7) Better developer UX: findings that are actionable, not accusatory
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Stella Ops can present findings like engineering wants to see them:
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* “This new dependency bump added X, removed Y”
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* “Here’s the minimal path from your code to the vulnerable call”
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* “Here’s the exact commit / layer / symbol change that made risk go up”
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That framing turns security into debugging, which engineers are already good at.
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## 8) Standards alignment without being “standards only”
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Stella Ops can speak the language auditors and customers care about:
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* SBOM-friendly (CycloneDX is a BOM standard; it’s also published as ECMA-424). ([GitHub][4])
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* Supply chain framework alignment (SLSA describes controls/guidelines to prevent tampering and improve integrity). ([SLSA][5])
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* Attestations that fit modern ecosystems (DSSE, in-toto style envelopes, sigstore verification).
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The advantage is you’re not just “producing an SBOM”—you’re producing **decisions + proofs** that are portable.
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## 9) Defensibility: a compounding knowledge graph
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Every scan produces structured facts:
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* What changed
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* What functions exist
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* What call paths exist
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* What was concluded, when, and based on what evidence
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Over time that becomes a proprietary, high-signal dataset:
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* Faster future triage (because prior context is reused)
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* Better suppression correctness (because it’s anchored to symbols/paths, not text matching)
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* Better cross-repo correlation (“this vulnerable sink shows up in 12 services, but only 2 are reachable”)
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## 10) “Ops” is the product: governance, exceptions, expiry, and drift control
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The last advantage is cultural: Stella Ops isn’t just a scanner, it’s a **risk operations system**:
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* time-bound suppressions that auto-expire
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* policy-as-code gates that understand reachability and diffs
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* evidence-backed exceptions (so you don’t re-litigate every quarter)
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---
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### A crisp way to pitch it internally
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**Stella Ops turns vulnerability management from a static list of CVEs into a living, evidence-backed change log of what actually matters—and why.**
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Delta scanning cuts noise, call-stack analysis makes prioritization real, and DSSE/VEX-style artifacts make every decision auditable. ([CISA][2])
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[1]: https://github.com/secure-systems-lab/dsse?utm_source=chatgpt.com "DSSE: Dead Simple Signing Envelope"
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[2]: https://www.cisa.gov/resources-tools/resources/minimum-requirements-vulnerability-exploitability-exchange-vex?utm_source=chatgpt.com "Minimum Requirements for Vulnerability Exploitability ..."
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[3]: https://cyclonedx.org/capabilities/vex/?utm_source=chatgpt.com "Vulnerability Exploitability eXchange (VEX)"
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[4]: https://github.com/CycloneDX/specification?utm_source=chatgpt.com "CycloneDX/specification"
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[5]: https://slsa.dev/?utm_source=chatgpt.com "SLSA • Supply-chain Levels for Software Artifacts"
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