github Python analyzed 278f41b

balanced7/akashic-aurora

github

Memory for AI agents that learns what actually helped: lessons injected at the moment of action, credited by outcome, challenged by counter-evidence. Local-first, multi-agent, test-gated.

maintainer
balanced7
license
Apache-2.0
first seen
2026-07-02
last seen
2026-08-01
releases · 30d
0
short id
risk 51/100 · heuristic grade
C elevated
  • capability exposure inferred + 26
  • recent drift inferred + 20
  • tool safety inferred + 5

inferred

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 1m ago · see ecosystem CVEs →

risk trajectory 2 movements
  • D · 60 C · 51
  • C · 52 D · 60
capability exposure grade factor +26
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: eval()/exec()

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

analyzed commit 278f41b · analyzer v28 · 2d ago

skills & prompt files 11

danger signals4

other grade factors evidence elsewhere
embed badge readme-ready
live risk-grade badge preview [![MCP Observatory risk grade](https://mcpobservatory.com/servers/github:balanced7/akashic-aurora/badge.svg)](https://mcpobservatory.com/servers/github:balanced7/akashic-aurora/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 balanced7.