Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq), deploying/serving models (use deployment), or comparing completed baseline-vs-quantized results (use compare-results).
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Low
Low-risk findings worth noting
Low
Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
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.
SKILL.md:110-138 describes fetching and reading (outsider-authored) web sources such as recipes.vllm.ai pages and HuggingFace model cards at runtime, and those fetched free-text pages are then used to write LLM-relevant configuration, creating an indirect prompt-injection exposure path.
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.
Flagged because the skill explicitly instructs fetching runtime content from recipes.vllm.ai/<org>/<model> to derive deployment/serve flags and shows an example pre_cmd that curls https://huggingface.co/.../reasoning_parser.py (downloadable code) which would be fetched/executed at runtime, so external content can directly control prompts/commands or execute code.
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