Search, read, and query research papers via Feynman's alphaXiv-backed alpha tools. Use when the user asks about academic papers, wants to find research on a topic, needs to read a specific paper, ask questions about a paper, inspect a paper's code repository, or manage paper annotations.
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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.
The REQUIRED workflow is to use the `feynman alpha get`/`ask`/`code` commands to fetch and read paper content (e.g., from arXiv or a GitHub repo at runtime), which is outsider-authored free text that the LLM would ingest as document text for Q&A, making indirect prompt-injection possible.
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.
The example command "feynman alpha code https://github.com/karpathy/nanoGPT src/model.py" (https://github.com/karpathy/nanoGPT) shows a runtime fetch of GitHub repo files which can be injected into the agent's context and thus directly influence prompts.
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