brin

credit score for context

       

---

ai agents are only as safe as the context they consume. brin scores each piece before your agent acts, detecting malware, prompt injection, phishing, and supply chain attacks across packages, repos, mcp servers, skills, and urls.

this dataset contains open-source threat scan records from brin's scoring pipeline. free for research, red-teaming, and model training.

• docs: brin.sh/docs
• cli: brin-cli
• cursor plugin: included in this repo (see plugins/cursor)

---

schema

each record is a single brin scan result. the fields are:

field	type	description
origin	string	source type: npm, pypi, crate, domain, page, repo, skill, mcp, contributor, commit, pr
identifier	string	identifier within the origin (e.g. express, example.com)
version	string	version or ref (optional)
score	integer	0–100 safety score. higher is safer
confidence	string	low, medium, or high
verdict	string	safe, caution, suspicious, or malicious
sub_scores	object	breakdown across four dimensions (see below)
threats	array	detected threat signals with type and description (optional, omitted if none)
scanned_at	string	ISO 8601 timestamp of when the scan was run

sub_scores

dimension	description
identity	publisher reputation, domain age, ownership signals
behavior	runtime behavior, network calls, install scripts
content	source code, prompt content, instruction analysis
graph	dependency graph, transitive risk, maintainer overlap

example record

{
  "origin": "npm",
  "identifier": "express",
  "version": "4.18.2",
  "score": 81,
  "confidence": "medium",
  "verdict": "safe",
  "sub_scores": {
    "identity": 95.0,
    "behavior": 40.0,
    "content": 100.0,
    "graph": 30.0
  },
  "scanned_at": "2026-02-25T09:00:00Z"
}

---

coverage

origin	what is scored	threats detected
npm / pypi / crate	open source packages	install-time attacks, credential harvesting, typosquatting
domain / page	websites and web pages	prompt injection, phishing, cloaking, exfiltration via hidden content
repo	github repositories	agent config injection, malicious commits, compromised dependencies
skill	agent skills	description injection, output poisoning, instruction override
mcp	mcp servers	tool shadowing, schema abuse, silent capability escalation
contributor	github contributors	impersonation, typosquatting, suspicious commit patterns
commit	github commits	malicious code injection, backdoor insertion, unauthorized changes
pr	github pull requests	supply chain attacks via PRs, malicious contributions, social engineering

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format

records are stored as jsonl (newline-delimited json) - one record per line. this makes the dataset trivially streamable and parseable without loading everything into memory.

files are organized by origin under data/:

data/
  npm.jsonl
  pypi.jsonl
  crate.jsonl
  domain.jsonl
  page.jsonl
  repo.jsonl
  skill.jsonl
  mcp.jsonl
  contributor.jsonl
  commit.jsonl
  pr.jsonl

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contributing

see CONTRIBUTING.md for details.

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license

MIT

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_{built by superagent - ai security for the agentic era}