langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
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Low
Low-risk findings worth noting
Low
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 shown in SKILL.md uses LangSmith “hub prompts” by calling `client.pull_prompt(...)` at runtime and then `prompt.invoke(...)`, meaning any outsider-authored free text from that prompt (hub content) can be ingested into the LLM context via `prompt.invoke`.
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
Examples show pulling prompts and writing runs at runtime to LangSmith API endpoints (e.g., fetching hub prompts via the client), and those endpoints are explicitly referenced such as https://api.smith.langchain.com and the multi-tenant endpoints https://api-team1.langsmith.com and https://api-team2.langsmith.com which can deliver prompt templates that directly control agent behavior.
- Repository
- Orchestra-Research/AI-Research-SKILLs
- Commit
773a529
- Audited
- Security analysis
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