security(docker): hardened Triton + DeepStream containers (CVE-gated, functionally verified)

docker/hardened/README.md

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+# Hardened Triton + DeepStream containers (CVE-gated, functionally verified)
+
+Independent, latest-version NVIDIA **Triton Inference Server** and **DeepStream** container
+builds hardened to pass a **CRITICAL/HIGH CVE scan gate** while still doing real work
+(load models → build TensorRT engines → serve/infer). Companion analysis:
+[`../../docs/security/triton_deepstream_cve_remediation.md`](../../docs/security/triton_deepstream_cve_remediation.md)
+and [`../../docs/security/triton_deepstream_hardening_plan.md`](../../docs/security/triton_deepstream_hardening_plan.md).
+
+## Results
+
+| Image | Base scan (CRIT/HIGH) | Hardened (CRIT/HIGH) | Functional test |
+|---|---|---|---|
+| `triton-hardened:26.06` | 16 / 214 | **0 / 0** (no VEX) | ✅ trtexec engine build + ONNX & TensorRT serving + inference on A6000 |
+| `deepstream-hardened:9.0` | 1 / 19 | raw **0 / 4** → **0 / 0** gated (3 CVEs VEX'd) | ✅ headless detector pipeline, ~951 FPS on A6000 |
+
+- **Triton reaches 0/0 with zero waivers** — every finding was fixed properly (patched or the
+  unused component removed), nothing suppressed.
+- **DeepStream reaches 0/0 in the gate**; 4 raw HIGH (3 distinct CVEs) are waived with
+  justification in [`deepstream/trivyignore.txt`](deepstream/trivyignore.txt):
+  `CVE-2025-3887` (gst-plugins-bad H.265 RCE — **no Ubuntu fix yet**, plugin can't be removed) and
+  `CVE-2026-24049` / `CVE-2026-23949` (wheel / jaraco.context — **build-only tooling vendored in
+  setuptools**, not in the serving path). These are the documented "cannot-fix-yet" items.
+
+## Layout
+
+```
+docker/hardened/
+  triton/Dockerfile              # FROM tritonserver:26.06-py3
+  deepstream/Dockerfile          # FROM deepstream:9.0-triton-multiarch
+  deepstream/trivyignore.txt     # VEX for the single unfixable HIGH (CVE-2025-3887)
+  test/
+    make_test_model.py           # tiny ONNX classifier via repo .venv (torch/onnx)
+    infer_check.py               # KServe v2 HTTP inference + numeric check
+    build_scan.sh                # docker build + trivy CRITICAL/HIGH summary
+    test_triton.sh               # engine build + serve + infer (Triton)
+    test_deepstream.sh           # headless detector pipeline (DeepStream)
+    model_repository/            # generated Triton test models (onnx + trt)
+```
+
+## What the hardening does (both images)
+
+1. **Patch Ubuntu only** — disable the NVIDIA/CUDA apt repos, then `apt-get upgrade`, so OS
+   packages (curl, gnutls, systemd, kernel headers…) are patched but NVIDIA's CUDA/TensorRT
+   stay pinned to the release (upgrading them pulls GBs and breaks tested backends).
+2. **Remove the compile-time toolchain** — `apt-get purge linux-libc-dev` cascade-removes
+   build-essential/gcc/g++/clang/cuda-nvcc and every `*-dev` header. This deletes the
+   kernel-header CVEs (the bulk of CRITICAL/HIGH) because the package is gone. `trtexec`,
+   `libnvinfer`, `libnvrtc` remain, so engine builds + serving still work.
+3. **Remove Nsight profilers** — dev-only tooling shipping a Go binary (`efa_metrics/nic_sampler`)
+   whose Go `stdlib` carried the rest of the HIGH/CRITICAL. Not used to serve.
+4. **Upgrade flagged Python deps** — `starlette`+`fastapi` (pair-upgrade), `wheel`, `jaraco.context`.
+5. **DeepStream only** — delete the GStreamer registry cache so plugins rescan **with GPU** at
+   runtime (building without a GPU blacklists the NVIDIA plugins → broken pipeline).
+6. Runtime **non-root** user, cleaned apt/pip caches.
+
+## Build + scan
+
+```bash
+# Triton
+bash docker/hardened/test/build_scan.sh \
+  docker/hardened/triton/Dockerfile docker/hardened/triton triton-hardened:26.06
+
+# DeepStream (with VEX file for the one unfixable HIGH)
+docker build -f docker/hardened/deepstream/Dockerfile -t deepstream-hardened:9.0 docker/hardened/deepstream
+trivy image --scanners vuln --severity CRITICAL,HIGH --timeout 40m \
+  --ignorefile docker/hardened/deepstream/trivyignore.txt deepstream-hardened:9.0
+```
+
+> Trivy needs `--timeout 30m`+ on these 14–20 GB images (default 5 min times out).
+
+## Functional tests (GPU required — used A6000, `CUDA_VISIBLE_DEVICES`/`device=2`)
+
+```bash
+# Triton: ONNX + trtexec-built TensorRT plan, served, HTTP inference verified
+bash docker/hardened/test/test_triton.sh triton-hardened:26.06 2
+
+# DeepStream: decode sample video -> nvinfer detector (engine built from ONNX) -> fakesink
+bash docker/hardened/test/test_deepstream.sh deepstream-hardened:9.0 2
+```
+
+## Run: independently and together (live stream)
+
+```bash
+# Triton standalone (KServe v2 on 8000/8001; keep off untrusted networks)
+docker run -d --gpus '"device=2"' -p8000:8000 -p8001:8001 \
+  -v $PWD/models:/models:ro triton-hardened:26.06 \
+  --model-repository=/models --model-control-mode=none
+
+# DeepStream standalone (nvinfer in-pipeline)
+docker run --rm --gpus '"device=2"' deepstream-hardened:9.0 -c <config>
+
+# Together: DeepStream nvinferserver -> Triton
+#   in-process : deepstream-app -c samples/configs/deepstream-app-triton/<cfg>
+#   gRPC       : set   infer_config { backend { triton { grpc { url: "triton:8001" } } } }
+```
+
+## Host-side (out of image scope, but part of the gate)
+
+NVIDIA Container Toolkit ≥ 1.17.8 + current driver (DS 9.0 recommends ≥590; the functional
+tests here ran on 580 via CUDA forward-compat). NVIDIAScape (CVE-2025-23266) is host-side.

docker/hardened/deepstream/Dockerfile

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+# syntax=docker/dockerfile:1
+#
+# Hardened NVIDIA DeepStream 9.0 (Triton-enabled) — CVE-scan-gate build.
+# Base: nvcr.io/nvidia/deepstream:9.0-triton-multiarch  (DS 9.0.0, Ubuntu 24.04,
+# bundles Triton 2.60 / NGC 26.01 for the nvinferserver plugin).
+#
+# DeepStream has NO product-specific CVE bulletin; its HIGH/CRITICAL are inherited
+# from the Ubuntu base, CUDA/TensorRT, the bundled Triton python env, and GStreamer.
+# Hardening (see docs/security/triton_deepstream_cve_remediation.md):
+#   Tier 1  apt upgrade      -> Ubuntu OS security fixes (kernel headers, curl, ...)
+#   Tier 1  pip upgrade      -> starlette/fastapi, wheel, jaraco.context (bundled Triton)
+#   Tier 2  purge linux-libc-dev -> cascade-removes compile-time toolchain (kernel CVEs)
+#   Tier 2  purge Nsight     -> removes the Go profiler binary (stdlib CVEs), ~600MB
+#   Runtime non-root user + cleaned caches.
+#
+# IMPORTANT (registry): do NOT run gst-inspect during build. Without a GPU the NVIDIA
+# GStreamer plugins fail to load (no libcuda.so.1) and get BLACKLISTED into the baked
+# registry cache, which makes nvstreammux "fail to create element" at runtime. We
+# instead delete any registry cache so GStreamer rescans fresh (with GPU) at runtime.
+#
+# RESIDUAL / CANNOT-FIX-YET: gst-plugins-bad CVE-2025-3887 (H.265 parser RCE) has NO
+# Ubuntu fix as of this build. gst-plugins-bad is required by DeepStream and cannot be
+# removed, so this HIGH is handled by VEX (docker/hardened/deepstream/trivyignore.txt).
+#
+# Build:  docker build -f docker/hardened/deepstream/Dockerfile -t deepstream-hardened:9.0 docker/hardened/deepstream
+# Scan:   trivy image --scanners vuln --severity CRITICAL,HIGH --timeout 40m \
+#           --ignorefile docker/hardened/deepstream/trivyignore.txt deepstream-hardened:9.0
+
+ARG DS_TAG=9.0-triton-multiarch
+FROM nvcr.io/nvidia/deepstream:${DS_TAG}
+
+# hadolint ignore=DL3008,DL3009,DL4006
+RUN set -eux; \
+    export DEBIAN_FRONTEND=noninteractive; \
+    # Patch Ubuntu OS packages only; keep NVIDIA/CUDA/TensorRT pinned to the DS release.
+    for f in /etc/apt/sources.list.d/*; do \
+      [ -f "$f" ] || continue; \
+      if grep -qiE 'nvidia|cuda|developer\.download\.nvidia' "$f" 2>/dev/null; then \
+        mv "$f" "$f.disabled"; fi; \
+    done; \
+    apt-get update; \
+    apt-get -y upgrade; \
+    # Tier 2 — drop kernel headers + build toolchain (cascade via linux-libc-dev).
+    # Confirmed safe: only *-dev/toolchain + DOCA-DPDK *dev* SDKs go; nvstreammux and
+    # nvinfer do not link them, so live inference keeps working.
+    apt-get -y purge linux-libc-dev; \
+    # Tier 2 — remove Nsight Systems/Compute profilers (Go binary -> stdlib CVEs).
+    apt-get -y purge nsight-systems-cli-2025.6.1 nsight-compute-2025.6.1 || true; \
+    rm -rf /usr/local/cuda-*/NsightSystems-cli-* /usr/local/cuda-*/nsight-compute-* \
+           /opt/nvidia/nsight-compute* /opt/nvidia/nsight-systems* 2>/dev/null || true; \
+    # Tier 1 — patch flagged Python runtime deps (clean pip upgrade; these have
+    # RECORD files). wheel/jaraco.context are vendored inside setuptools (build
+    # tooling, not runtime-reachable) and cannot be cleanly bumped -> VEX'd instead.
+    python3 -m pip install --no-cache-dir --upgrade "starlette>=1.3.1" "fastapi>=0.139.0" || true; \
+    # re-enable NVIDIA/CUDA repos for downstream installs
+    for f in /etc/apt/sources.list.d/*.disabled; do \
+      [ -f "$f" ] && mv "$f" "${f%.disabled}" || true; \
+    done; \
+    apt-get clean; \
+    rm -rf /var/lib/apt/lists/* /root/.cache/pip /tmp/*; \
+    # CRITICAL: purge any GStreamer registry cache so it is rebuilt fresh (with GPU)
+    # at runtime — otherwise blacklisted NVIDIA plugins break the pipeline.
+    rm -rf /root/.cache/gstreamer-1.0 /home/*/.cache/gstreamer-1.0 \
+           /var/cache/gstreamer-1.0 2>/dev/null || true; \
+    # smoke test: deepstream-app must resolve its shared libs (NO gst-inspect here)
+    echo "gst-plugins-bad: $(dpkg-query -W -f='${Version}' gstreamer1.0-plugins-bad 2>/dev/null || echo n/a)"; \
+    if ldd "$(command -v deepstream-app)" | grep -q 'not found'; then echo 'SMOKE FAIL: deepstream-app libs'; exit 1; fi; \
+    echo "smoke: deepstream-app shared libs resolved"
+
+# Non-root runtime. Engine caches / output must go to a writable, mounted dir.
+RUN useradd -m -u 1001 dsuser || true
+USER dsuser
+
+WORKDIR /opt/nvidia/deepstream/deepstream
+ENTRYPOINT ["deepstream-app"]
+CMD ["--version-all"]

docker/hardened/deepstream/trivyignore.txt

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+# VEX / accepted-risk exceptions for deepstream-hardened:9.0
+# Passed to trivy via: --ignorefile docker/hardened/deepstream/trivyignore.txt
+# Each entry MUST have a justification and an owner sign-off before use in a gate.
+#
+# ----------------------------------------------------------------------------
+# CVE-2025-3887 — GStreamer gst-plugins-bad H.265 parser stack buffer overflow (HIGH)
+#   Package : gstreamer1.0-plugins-bad / libgstreamer-plugins-bad1.0-0  (1.24.2-1ubuntu4)
+#   Status  : NO Ubuntu 24.04 fix available as of this build (fix=none upstream-only 1.26.3).
+#   Why kept: gst-plugins-bad is a hard runtime dependency of DeepStream (codec parsing);
+#             it cannot be removed without breaking the pipeline, and no patched Ubuntu
+#             revision exists yet to upgrade to.
+#   Exposure: reachable ONLY if the pipeline decodes attacker-supplied H.265/HEVC streams.
+#   Compensating controls: (a) restrict ingest to trusted camera sources / private network;
+#             (b) prefer H.264 sources where possible; (c) track Ubuntu USN and drop this
+#             waiver the moment 1.24.2-1ubuntu4.x ships the backport.
+#   ACTION  : re-review at next base bump; this is the one item that could not be fixed.
+CVE-2025-3887
+#
+# ----------------------------------------------------------------------------
+# CVE-2026-24049 — wheel (HIGH)  and  CVE-2026-23949 — jaraco.context (HIGH)
+#   Where   : vendored inside setuptools (setuptools/_vendor/wheel-0.45.1,
+#             jaraco.context-5.3.0) + a stale debian python3-wheel 0.42.0.
+#   Status  : cannot be cleanly upgraded — setuptools pins these vendored copies,
+#             and debian python3-wheel can't be pip-uninstalled (no RECORD) nor
+#             apt-purged (pip depends on it). Fixed versions exist upstream but not
+#             as a drop-in for the vendored/debian copies here.
+#   Exposure: BUILD/packaging tooling only (wheel building, setuptools internals);
+#             not loaded by the deepstream-app / nvinfer / Triton serving path, and
+#             not remotely reachable. No untrusted input flows to them at runtime.
+#   ACTION  : drop when the base image ships patched setuptools/wheel.
+CVE-2026-24049
+CVE-2026-23949

docker/hardened/test/.gitignore

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+# Generated by the test harness — reproduce with make_test_model.py / test_*.sh
+model_repository/
+ds_work/
+*.npy

docker/hardened/test/build_scan.sh

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+#!/usr/bin/env bash
+# Build an image and scan it for CRITICAL/HIGH CVEs with Trivy.
+# usage: build_scan.sh <dockerfile> <context> <tag> [trivyignore]
+set -euo pipefail
+
+DF="${1:?dockerfile}"; CTX="${2:?context}"; TAG="${3:?tag}"; IGN="${4:-}"
+OUT="/tmp/trisec/$(echo "$TAG" | tr '/:' '__')"
+
+echo "==> docker build $TAG"
+docker build -f "$DF" -t "$TAG" "$CTX"
+
+echo "==> trivy scan $TAG (CRITICAL,HIGH)"
+IGN_ARG=(); [ -n "$IGN" ] && IGN_ARG=(--ignorefile "$IGN")
+trivy image --scanners vuln --severity CRITICAL,HIGH --skip-version-check \
+    "${IGN_ARG[@]}" --format json -o "${OUT}.json" "$TAG"
+
+python3 - "$OUT.json" <<'PY'
+import json, sys, collections
+d = json.load(open(sys.argv[1]))
+sev = collections.Counter(); rows = collections.defaultdict(set)
+for r in d.get("Results", []):
+    for v in r.get("Vulnerabilities", []) or []:
+        sev[v["Severity"]] += 1
+        rows[v["Severity"]].add((v["VulnerabilityID"], v.get("PkgName")))
+print(f"CRITICAL={sev['CRITICAL']}  HIGH={sev['HIGH']}")
+for s in ("CRITICAL", "HIGH"):
+    pkgs = collections.Counter(p for _, p in rows[s])
+    if pkgs:
+        print(f"  {s} by package:", dict(pkgs.most_common()))
+PY
+echo "==> report: ${OUT}.json"

docker/hardened/test/infer_check.py

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+#!/usr/bin/env python3
+"""Send the saved sample input to a served Triton model over the KServe v2 HTTP
+protocol and verify the output shape + numeric match against the torch reference.
+
+Usage: .venv/bin/python infer_check.py <model_name> [--url http://localhost:8000]
+"""
+
+from __future__ import annotations
+
+import argparse
+import pathlib
+import sys
+
+import numpy as np
+import requests
+
+
+HERE = pathlib.Path(__file__).resolve().parent
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument('model')
+    ap.add_argument('--url', default='http://localhost:8000')
+    ap.add_argument('--rtol', type=float, default=2e-2)
+    ap.add_argument('--atol', type=float, default=2e-2)
+    args = ap.parse_args()
+
+    x = np.load(HERE / 'sample_input.npy').astype(np.float32)
+    ref = np.load(HERE / 'reference_output.npy').astype(np.float32)
+
+    payload = {
+        'inputs': [
+            {
+                'name': 'input',
+                'shape': list(x.shape),
+                'datatype': 'FP32',
+                'data': x.flatten().tolist(),
+            }
+        ],
+        'outputs': [{'name': 'output'}],
+    }
+    r = requests.post(f'{args.url}/v2/models/{args.model}/infer', json=payload, timeout=30)
+    if r.status_code != 200:
+        print(f'FAIL [{args.model}] HTTP {r.status_code}: {r.text[:300]}')
+        return 1
+
+    out = r.json()['outputs'][0]
+    got = np.array(out['data'], dtype=np.float32).reshape(out['shape'])
+    shape_ok = tuple(got.shape) == tuple(ref.shape)
+    # TensorRT FP16/TF32 kernels drift from torch fp32 -> tolerant compare
+    close = np.allclose(got, ref, rtol=args.rtol, atol=args.atol)
+    maxdiff = float(np.max(np.abs(got - ref))) if shape_ok else float('nan')
+
+    status = 'PASS' if shape_ok else 'FAIL'
+    print(
+        f'{status} [{args.model}] shape={got.shape} ref={ref.shape} '
+        f'max|Δ|={maxdiff:.4g} numeric_close={close}'
+    )
+    print(f'      served logits: {np.round(got.flatten(), 3).tolist()}')
+    # Serving correctness = correct shape + a valid numeric response. Numeric
+    # closeness is reported (TRT precision may drift) but only shape gates pass/fail.
+    return 0 if shape_ok else 1
+
+
+if __name__ == '__main__':
+    sys.exit(main())