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docs: working with non-Anthropic models - #11

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docs: working with non-Anthropic models#11
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schenksj:docs-non-anthropic-models

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Adds docs/working-with-non-anthropic-models.md — how to run the VulnHunter skills on non-Anthropic models via OpenRouter, and which models actually work, grounded in a cross-model evaluation.

Contents

  • How it works — OpenRouter's Anthropic-compatible endpoint, pinning the main agent + aliases + subagents to one model, and blocking Bash for a static (not merely read-only) run, with the containment rationale for untrusted third-party/open-weight models.
  • Quick start — key handling without shell-history leakage, pre-creating the results dir + metadata VulnHunter can't collect while Bash is blocked, and the invocation that opts into a non-recommended model past the model gate.
  • Recommended models — Opus 4.8 (report quality/consistency) and GLM-5.2 (severity-weighted coverage/value), each with its evaluated weakness and the "run GLM 2–3× and union" caveat; plus the objectivity caveat (Opus adjudicated while under evaluation).
  • Completed evaluations not recommended — Kimi (noisy/slow), Nemotron (incomplete/low-precision), Gemma (failed).
  • Model criteria — baseline (1M+ context, instruction-following, reasoning, code understanding) + eval-derived discriminators (agentic subagent orchestration, long-horizon stamina, output consistency & valid citations, trust-boundary judgment & false-positive discipline).
  • Roadmap — OpenAI frontier models planned under OpenAI's trusted cyber program (separate access path), not yet evaluated; DeepSeek eval to finish.

🤖 Generated with Claude Code

Add docs/working-with-non-anthropic-models.md covering the
~/.local/bin/openrouter_claude wrapper (OpenRouter env overrides, single-model
tier pinning, read-only-by-default because Bash is omitted, Agent as the
load-bearing tool), a quick start, and the currently recommended models
(Opus 4.8 native, GLM-5.2 via OpenRouter) with the not-recommended set and the
reasons (Kimi noisy/slow, Nemotron incomplete, Gemma failed).

Documents the criteria a model must meet, grounded in the cross-model eval:
baseline (1M+ context, instruction-following, reasoning, code understanding)
plus the eval-derived discriminators (agentic subagent orchestration,
long-horizon stamina, output consistency & valid citations, trust-boundary
judgment & false-positive discipline). Notes OpenAI frontier models are planned
under OpenAI's trusted cyber program (separate access path) and not yet tested.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
@schenksj
schenksj requested a review from a team as a code owner July 18, 2026 18:53
schenksj and others added 2 commits July 19, 2026 15:53
Fold in two new cross-model evaluation runs:

- Kimi K3 (moonshotai/kimi-k3): now recommended on a single run — led the
  corpus on true-positive count and cost-effectiveness with strong precision,
  and uniquely discovered two exploitable High-severity vulnerabilities no
  other model found (a dot-segment token path-prefix bypass and an ambient-
  credential scp-form clone). Caveats noted: single run, ~5.5 h, and its severe
  coverage is credit for vulns it discovered. Distinguished from the older,
  superseded Kimi k2.7-code.
- Qwen 3.7 Max (qwen/qwen3.7-max): not recommended — strong recon but the hunt
  stage mass-dismissed nearly every real finding (1 TP + 1 FP vs 37 known
  vulns, missing all five Highs) despite 40 subagents and 1,100+ tool calls;
  also self-mislabeled its model and declared the codebase "well-hardened."

Updates the TL;DR accordingly. Full data in eval_rep/EVAL_REPORT.md.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds Opus 5 (11th evaluated run) to the recommended-models table and TL;DR.
32 adjudicated-real findings at 0.865 precision — more than double any other
model — but $110.07/run and third place when scored only on the vulnerabilities
every model was measured against.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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