github Python analyzed 9f0e37f

prismal-ai/prismal

github

Prism-inspired multi-agent orchestration framework built on LangGraph. Security-first, provider-agnostic, composable.

maintainer
prismal-ai
license
MIT
first seen
2026-06-06
last seen
2026-07-20
releases · 30d
5
short id
risk 49/100 · heuristic grade
C elevated
  • capability exposure inferred + 35
  • recent drift inferred + 12
  • tool safety inferred + 5
  • trust mitigators mixed − 3

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.

graded 6m ago · see ecosystem CVEs →

risk trajectory 2 movements
  • B · 32 C · 49
  • A · 0 B · 32
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 +5
  1. medium dangerous code

    dynamic exec: __import__(), __import__ sink

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

analyzed commit 9f0e37f · analyzer v28 · 1d ago

skills & prompt files 2

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:prismal-ai/prismal/badge.svg)](https://mcpobservatory.com/servers/github:prismal-ai/prismal/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 prismal-ai.