github Python analyzed 9893493 deep scan · partial (315/2,422 files) unconfirmed MCP

Kiln-AI/Kiln

github

Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.

maintainer
Kiln-AI
licence
NOASSERTION
first seen
2026-06-01
last seen
2026-09-10
releases · 30d
1
short id
risk 59/100 · heuristic grade
C elevated inferred analyzer v33 (current)
  • capability exposure inferred + 35
  • recent drift inferred + 20
  • tool safety inferred + 12
  • trust mitigators mixed − 8

inferred mixed

The A–E grade is our heuristic synthesis — a "review this" prompt, not a verdict. Each factor is tagged by what backs it: attested (a verifiable record), reported (a third party's claim), or inferred (our own heuristic, e.g. permissions). See methodology.

grade last moved 3d ago · see ecosystem CVEs →

risk trajectory 2 movements
  • C · 51 C · 59
  • A · 0 C · 51
capability exposure grade factor +35
Inferred surface — each links to servers holding it:
vulnerabilities 0 CVEs

No known CVEs for this server.

tool safety 1 findings · grade factor +12
  1. high dangerous code

    dynamic exec: eval()/exec(), pickle.loads()

skills & danger signals github-tarball
prompt-surface shipped agent-instruction files + hidden-content / dangerous-code findings — quoted from the analyzed source

analyzed commit 9893493 · analyzer v33 · 2h ago

skills & prompt files 11

danger signals5

other grade factors evidence elsewhere
embed badge readme-ready
live risk-grade badge preview [![MCP Observatory risk grade](https://mcpobservatory.com/servers/github:Kiln-AI/Kiln/badge.svg)](https://mcpobservatory.com/servers/github:Kiln-AI/Kiln/security)

Heuristic, inferred signals — false positives (legitimately powerful tools, forks, language ports) are expected. Treat each as "review this", not a verdict. See the ecosystem-wide picture on the security hub, or the fleet security of Kiln-AI.