sparse-autoencoder-training
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
The required runtime workflow “training a custom SAE” references using an external dataset path (`dataset_path="monology/pile-uncopyrighted"`) which can cause the agent to ingest outsider-authored free-form text from that dataset into the LLM-related training/processing context.
- Repository
- NousResearch/hermes-agent
- Commit
4c9628e
- Audited
- Security analysis
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