github Python analyzed fba3936

NuGuardAI/nuguard

github

AI red-teaming tool and LLM security framework to evaluate agentic AI applications. Tests prompt injections, handles vulnerability assessment, SBOM generation, and static analysis.

maintainer
NuGuardAI
licence
NOASSERTION
first seen
2026-07-31
last seen
2026-09-10
releases · 30d
4
short id
risk 64/100 · heuristic grade
D high inferred analyzer v33 (current)
  • capability exposure inferred + 35
  • recent drift inferred + 20
  • tool safety inferred + 12
  • 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.

grade last moved 3d ago · see ecosystem CVEs →

risk trajectory 4 movements
  • C · 56 D · 64
  • C · 37 C · 56
  • C · 57 C · 37
  • C · 49 C · 57
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: unsafe yaml.load(), 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 fba3936 · analyzer v33 · 6h ago

skills & prompt files 28

danger signals17

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