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# Meta-audit — the Claude Code session that performed these audits

A self-audit, completing the "audit the auditor" loop. Honest accounting of what this assistant has seen during the audit / analysis work and where that data went.

## Scope

This audit covers the Claude Code session running on the Proxmox host (`/root/.claude/projects/-root/`) on 2026-05-20 and 2026-05-21, from the message `> finish up for today. last task: the attached conversation was run on our lobehub…` onward. The work product of that session is the three audit reports in this directory.

## What the assistant processed

Input | Content | Source
--- | --- | ---
Transcript A | `75fb06e8-Lumen_TR004_Test_Run_Analysis.json` (255 KB, 60 messages, model `qwen3.5-397b-a17b`) | User-uploaded
Transcript B | `ec5fba44-Analyzing_LUMEN_TR004_Test_Data.json` (205 KB, 41 messages, model `qwen3-coder-30b-a3b-instruct`) | User-uploaded
SSH + `pct exec` on `nuc` | container env vars, `docker ps`, `proxy_server_config.yaml`, `gateway_tokens.json` (first chars only) | Live homelab inspection
`https://docs.hpc.gwdg.de/services/ai-services/saia/index.html` | SAIA public docs | `WebFetch` (Anthropic-mediated)

The Lumen transcripts contain DLR-context identifiers (`LUMEN`, `P3-Lampoldshausen`, `LOX/LCH4`, `TR-003/004/006`), anomaly metadata (`fuel turbopump vibration spike at t=8s`, `~12 g rms`), and chemistry/test-bench naming. The assistant quoted portions of these in chat output and in the audit reports.

## Where that data went

    flowchart LR
      user(["Operator"]) --> cc["Claude Code CLI<br/>on Proxmox host"]
      cc -->|every prompt + tool result<br/>+ assistant turn| api[(api.anthropic.com<br/>Anthropic API)]
      cc -->|ssh / pct / docker via shell| home["nuclide.systems<br/>(LAN-only)"]
      cc -->|WebFetch SAIA public docs| saiadocs[(docs.hpc.gwdg.de<br/>via Anthropic proxy)]
      cc -->|FLUX image-gen MCP| flux[(image-gen MCP backend<br/>Anthropic-side)]

      classDef leak fill:#5a2a2a,stroke:#c44,color:#fcc
      classDef ok fill:#234c2a,stroke:#4c8,color:#cfc
      classDef external fill:#5a4a2a,stroke:#cc8,color:#fec
      class api,flux leak
      class home ok
      class saiadocs external

This Claude Code session is itself an outbound channel. Every assistant turn, including those that quoted Lumen transcript content, was a request to `api.anthropic.com`. The full session is:

Channel | What flows | Trust
--- | --- | ---

Claude API (`api.anthropic.com`)

Full prompts + tool results + audit text the assistant produced. ~60+ turns this session. Includes verbatim LUMEN identifiers in audit body, snippets of transcript content, snippets of LiteLLM config, snippets of `gateway_tokens.json` (one token prefix), Vaultwarden plaintext secret (handed back to operator, then echoed in subsequent context).

Commercial vendor (Anthropic). Operator chose to use Claude Code; informed-consent leak.

`WebFetch` (also via Anthropic) | The URL `https://docs.hpc.gwdg.de/services/ai-services/saia/index.html` was fetched server-side by Anthropic, content returned to the assistant. Outbound from Anthropic to GWDG (public doc; no sensitive payload). | Public web fetch, no sensitive outbound payload.
Image-gen MCP | Prompt strings (containing brief LUMEN reference in one attempted illustration: "Python with LUMEN identifiers leaked to commercial cloud"). Backend is Anthropic-side per the MCP integration. | Same Anthropic boundary.
SSH / `pct` / shell | Authenticated to operator's own infrastructure. | ✅ LAN, no leak.
Sub-agents (Pocket-ID audit, CT inventory, etc.) | Ran on Anthropic's infrastructure — each got the relevant context for its narrow task. Same trust boundary as the main session. | Anthropic.

## Specific data this assistant sent to Anthropic

Across the audit work in this session, the following types of data were in the prompt/response stream to `api.anthropic.com`:

- Lumen test analysis identifiers: `LUMEN TR-004`, `P3-Lampoldshausen`, `LOX/LCH4`, `Fuel Turbopump Vibration Spike at t=8.0s`, `bearing replaced`, test campaign dates.
- Tool-call inventories (counts, MCP names, plugin identifiers) from both transcripts.
- A snippet of `gateway_tokens.json` content (the SUB UUID and one truncated token preview) when auditing the gateway.
- The plaintext Vaultwarden secret `HvUJYUxCGLrSoDY2g24NJI5anM70xXwcsd3e3itJ` (issued by the agent, surfaced in chat for the operator to save — operator saved it to Vaultwarden, but it remains in this session's context).
- Pocket-ID OIDC client identifiers (UUID prefixes, names, callback URLs).
- Internal IPs / CT layout (192.168.1.x).
- Snippet of LiteLLM `proxy_server_config.yaml` showing fallback chains.

## Threat model honesty

The act of running this audit using Claude Code is itself a higher-bandwidth leak than the leak it audited. Hours of conversation about LUMEN-context data went to Anthropic; the original Lumen TR-004 cloud-sandbox breach was ~1,500 lines of Python.

This is a deliberate trade-off: - ✅ Anthropic has stronger contractual guarantees than `codesandbox.io` (zero-data-retention API plans exist; Anthropic publishes a clear DPA). - ✅ The operator chose Claude Code consciously, knowing all prompts are API-bound. - ❌ It is not free. The work product is excellent; the data exposure is real.

## Mitigations for future audit work

Option | Pros | Cons
--- | --- | ---
Continue using Claude Code | Best-in-class tooling, doctrine + memory carry across sessions, sub-agents in parallel | All audit context goes to Anthropic
Use a local-only agent (e.g. `gpt-oss-120b` in workspace via Coder) | Zero off-host audit data | Far weaker capability; no rich tool-use; no memory; manual orchestration
Air-gap the work on a non-internet-connected host | Strongest privacy | No web fetches; no API; doctrine doesn't bootstrap; pace ~10x slower
Hybrid: Claude Code for general infra; air-gapped local agent for Lumen-specific prompts | Best of both | Process discipline required; operator decides routing per chat

For LUMEN-specific deep analysis going forward, the hybrid path is the rational one: do schema/topology/code work in Claude Code (no Lumen payload needed); do data-touching analysis in a Coder workspace with the `mcp-sandbox` template + a local LLM. The S3 artifact hub (planned) enables both surfaces to share visualization output without ever exposing raw data.

## Recommendations as session policy

1. For any chat that will reference Lumen/Shepard data, switch to the air-gapped / local-LLM path. Don't mix.
2. Strip secrets from chat replies aggressively. The Vaultwarden plaintext secret didn't need to be displayed; the agent could have written it to a tmpfs file and pointed the operator at it.
3. Tag this and future audit transcripts in `/docs/security/transcripts/` with a "contains DLR-context data" header so the policy is visible to anyone reviewing them.

See also: `data-leak-audit-comparison.md` (the cross-session comparison), `data-leak-audit-2026-05-20-tr004-cloud-sandbox.md`, `data-leak-audit-2026-05-21-tr004-artifacts.md`.