model-pruning
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.
63
75%
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Low
Low-risk findings worth noting
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tessl review fix ./skills/emerging-techniques/model-pruning/SKILL.mdLow
Low-risk findings.
2 low severity findings. 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 workflow includes fetching calibration text from a public dataset at runtime via `load_dataset(...)` (e.g., WikiText in `SKILL.md:343-345`), and that dataset’s free-form `text` is ingested as LLM-readable strings for activation computation, enabling indirect prompt-injection style contamination.
W012: Unverifiable external dependency detected (runtime URL that controls agent).
What this means
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
Why it was flagged
The skill instructs cloning and running external repositories which will fetch and execute remote code at runtime—e.g., https://github.com/locuslab/wanda and https://github.com/IST-DASLab/sparsegpt—making those runtime-executed dependencies high-risk.
- Repository
- OpenLAIR/dr-claw
- Commit
6da7f7c
- Audited
- Security analysis
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