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docs: drop Claude-preference claims from model guidance

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Remove the best-supported/recommended framing from the README
prerequisites and limitations, docs/safety.md, and docs/ai-providers.md.
The remaining caveat is model-neutral: capability varies, and a model
that does not follow Shannon's instructions or tool-use constraints
reliably produces weaker pentests.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
george-keygraph
2026-08-13 15:43:29 -07:00
co-authored by Claude Opus 5
parent 3f7d38de8f
commit 6c39101071
4 changed files with 10 additions and 10 deletions
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docs/ai-providers.md
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@@ -25,7 +25,7 @@ Shannon forwards only the selected provider's credential into the scan container
### Any other provider
Shannon accepts any provider and model present in the Pi harness catalogue. Browse them at [pi.dev/models](https://pi.dev/models). These are technically supported but not recommended. Claude models are best-supported (see the note below).
Shannon accepts any provider and model present in the Pi harness catalogue. Browse them at [pi.dev/models](https://pi.dev/models).
```bash
export SHANNON_AI_API_KEY=your-api-key # the provider's API key
@@ -37,7 +37,7 @@ This path covers providers whose credential is a single API key. Providers that
`npx @keygraph/shannon setup` exposes this as the **Other provider** option.
> [!IMPORTANT]
> Claude models are the best-supported option. Shannon's evaluations, internal testing, and agent harness are tuned for Claude. Other models are permitted and validated against the harness catalogue, but may not follow Shannon's instructions or tool-use constraints as reliably. Use them at your own risk.
> Models are validated against the harness catalogue, but capability varies. A model that does not follow Shannon's instructions or tool-use constraints reliably will produce weaker pentests. Evaluate the model you choose against your own targets before depending on its results.
## Cyber safeguards (do this before your first scan)
docs/safety.md
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@@ -28,7 +28,7 @@ For maximum isolation, run Shannon inside a disposable virtual machine.
## LLM and Automation Caveats
- **Verification is required**: Shannon uses a proof-by-exploitation methodology, but final reports can still contain weakly supported or incorrect details. Human review is essential.
- **Model support**: Shannon is officially supported only with Claude models. Alternative models may be incomplete, inaccurate, or unstable.
- **Model support**: results vary by model. A model that does not follow Shannon's instructions or tool-use constraints reliably may produce incomplete, inaccurate, or unstable runs.
- **Prompt injection risk**: Do not point Shannon at untrusted or adversarial codebases. AI-powered tools that read source code can be influenced by malicious repository content.
## Scope of Analysis