Post
178
✅ New Guide: OSS Supply Chain with SI Stack (v0.1)
Title:
🔐 OSS Supply Chain with the Structured Intelligence Stack — Patent‑Safe PoC Design (v0.1)
🔗 https://huggingface.co/blog/kanaria007/oss-supply-chain-v0-1
---
Summary:
Supply chain attacks (xz backdoor, malicious npm/PyPI packages) exploit blind spots
in commit → build → artifact chains. At 100+ transitive dependencies, human review
breaks down.
This guide shows how to eliminate blind spots using BuildAttestation + hermetic builds +
rollback marks. Covers binaries, containers, ML models—with ML framework dependencies
(PyTorch, TensorFlow, CUDA) getting special attention.
> From blind trust to causal proof.
> Deployable on existing CI/CD.
---
Why It Matters:
- *ML supply chain*: PyPI/conda packages with deep transitive deps (transformers, torch)
- *Hermetic builds*: pinned toolchains + deterministic modes (CUDA/cuDNN) → reproducible
- *SBOM enforcement*: coverage ≥99%; no SBOM → no publish
- *Rollback marks*: rebuild from trusted checkpoints when deps compromised
---
What's Inside:
*Foundation:*
BuildAttestation (causal chain), MalwareTombstone (proof-preserving removal),
Orchestrator API; hermetic builds with env_hash + toolchain pins
*ML specifics:*
Determinism profile (BLAS variant, CUDA deterministic, TensorFlow strict mode);
driver versions (CUDA/cuDNN/GPU); distributed training clock sync
*Compliance:*
SLSA 3+, Sigstore interop, SBOM (SPDX/CycloneDX); depth limits (max: 6);
policy gates (allow/deny/freeze)
*Integration:*
npm/PyPI/crates.io/Go examples; CI snippets (GitHub Actions, GitLab, Jenkins);
phased migration strategy
---
📖 Patent-Safe PoC Design
10 KPIs with SLA targets; 6-8 week PoC plan
Text: CC BY 4.0. Code: MIT.
---
For ML/AI systems—PyPI packages, model artifacts, containers—this provides
recipes and SLAs to ship supply chain security.
Title:
🔐 OSS Supply Chain with the Structured Intelligence Stack — Patent‑Safe PoC Design (v0.1)
🔗 https://huggingface.co/blog/kanaria007/oss-supply-chain-v0-1
---
Summary:
Supply chain attacks (xz backdoor, malicious npm/PyPI packages) exploit blind spots
in commit → build → artifact chains. At 100+ transitive dependencies, human review
breaks down.
This guide shows how to eliminate blind spots using BuildAttestation + hermetic builds +
rollback marks. Covers binaries, containers, ML models—with ML framework dependencies
(PyTorch, TensorFlow, CUDA) getting special attention.
> From blind trust to causal proof.
> Deployable on existing CI/CD.
---
Why It Matters:
- *ML supply chain*: PyPI/conda packages with deep transitive deps (transformers, torch)
- *Hermetic builds*: pinned toolchains + deterministic modes (CUDA/cuDNN) → reproducible
- *SBOM enforcement*: coverage ≥99%; no SBOM → no publish
- *Rollback marks*: rebuild from trusted checkpoints when deps compromised
---
What's Inside:
*Foundation:*
BuildAttestation (causal chain), MalwareTombstone (proof-preserving removal),
Orchestrator API; hermetic builds with env_hash + toolchain pins
*ML specifics:*
Determinism profile (BLAS variant, CUDA deterministic, TensorFlow strict mode);
driver versions (CUDA/cuDNN/GPU); distributed training clock sync
*Compliance:*
SLSA 3+, Sigstore interop, SBOM (SPDX/CycloneDX); depth limits (max: 6);
policy gates (allow/deny/freeze)
*Integration:*
npm/PyPI/crates.io/Go examples; CI snippets (GitHub Actions, GitLab, Jenkins);
phased migration strategy
---
📖 Patent-Safe PoC Design
10 KPIs with SLA targets; 6-8 week PoC plan
Text: CC BY 4.0. Code: MIT.
---
For ML/AI systems—PyPI packages, model artifacts, containers—this provides
recipes and SLAs to ship supply chain security.