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Plexavo

Plexavo

An open-source AWS misconfiguration scanner that reads your account with your own local AWS credentials — nothing is ever handed to anyone else. Run it yourself, the same way you'd run aws s3 ls, and get a 0-100 security score plus a plain-English report explaining exactly what's wrong, what an attacker would actually do with it, and the exact command to fix it.

Deterministic detection (pure Python/boto3, never AI) finds the misconfigurations. Claude only rewrites already-computed technical findings into a narrative a non-security founder can act on — it never decides what counts as a finding, and it's entirely optional (see Cost below).

What it checks

31 checks across 6 categories, all validated against real, live AWS accounts (not just offline logic) unless a specific, stated limitation made that impossible — see the individual docs/*-TEST-MATRIX.md files for exactly which checks are fully verified and which have a documented, structural reason they can't be (e.g. some checks can only prove their "clean" case without disabling a real security control to test the "bad" case).

Category Checks What it catches
IAM (iam.py, iam_hygiene.py) 14 Privilege escalation paths, wildcard admin, cross-account trust, root usage, dormant credentials
Network (network.py) 4 Security groups and RDS instances exposed to the internet
Storage (storage.py) 3 Public S3 buckets, via ACLs, bucket policies, or missing Block Public Access
Encryption (encryption.py) 3 Unencrypted EBS volumes, RDS instances, S3 default encryption
Logging (logging.py) 4 CloudTrail coverage/encryption, GuardDuty status
Usage analysis (usage.py) 2 (+1 skipped, duplicate of an IAM check) Granted permissions never actually used, roles nobody has assumed in 90+ days — the hardest category, built last, uses real CloudTrail history

Plus scoring.py (the 0-100 score) and plexavo/report/ai_narration.py (the opt-in AI layer).

Example output

Illustrative example (a public S3 bucket, an unencrypted volume, a role that's never been assumed) — this is what a scan actually surfaces, including a finding's free template remediation (shown by default, no --explain needed; --explain would replace this panel with a full AI narrative instead):

Your AWS Security Score: 74/100 (Good)

                                              Findings (3)
┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Check   ┃ Severity ┃ Resource                           ┃ Detail                                     ┃
┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ STOR-19 │ Critical │ arn:aws:s3:::taskflow-uploads-prod │ Bucket 'taskflow-uploads-prod' allows      │
│         │          │                                    │ public read via bucket policy.             │
│ ENC-29  │ Medium   │ vol-015506d04acb46ca9              │ EBS volume 'vol-015506d04acb46ca9' is not  │
│         │          │                                    │ encrypted at rest.                         │
│ USE-27  │ High     │ AWSSecurityScannerReadOnlyRole     │ Role 'AWSSecurityScannerReadOnlyRole' has  │
│         │          │                                    │ never been assumed since creation.         │
└─────────┴──────────┴────────────────────────────────────┴────────────────────────────────────────────┘

╭────────────────────────────────────────── source: template ──────────────────────────────────────────╮
│ STOR-19 — arn:aws:s3:::taskflow-uploads-prod                                                         │
│                                                                                                      │
│ IMPACT: Bucket 'taskflow-uploads-prod' doesn't have full S3 Block Public Access protection enabled.  │
│ Without this protection, a single mistake — an overly broad bucket policy, a public ACL grant,       │
│ someone copy-pasting a policy from a tutorial — immediately exposes every object to anyone on the    │
│ internet via a plain s3:GetObject call, with nothing left to catch the mistake.                      │
│                                                                                                      │
│ CONFIDENCE: Confirmed                                                                                │
│                                                                                                      │
│ NEXT STEP: Turn on Block Public Access now: aws s3api put-public-access-block --bucket               │
│ taskflow-uploads-prod --public-access-block-configuration                                            │
│ "BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true"       │
│                                                                                                      │
│ FULL FIX DETAIL: Enable all four Block Public Access settings unless there's a specific, documented  │
│ reason not to:                                                                                       │
│ aws s3api put-public-access-block --bucket taskflow-uploads-prod --public-access-block-configuration │
│ "BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true"        │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────╯

Severity, confidence, and evidence are always separate signals, never merged into one label — a low-confidence Critical and a high-confidence Medium don't read the same. This particular finding has no Evidence line because it's a directly-observed fact with nothing uncertain about it — Evidence only appears when a check has a concrete account-state fact behind it (e.g. an IAM policy scoped by a Condition block, or a CloudTrail lookup that hit its page cap on USE-26), and Confidence only drops from "Confirmed" in that same situation. --report-html/--report-pdf render this same data as a full report; see Cost for what --explain needs and what it costs.

Quick start

The install path depends on your OS — uv on macOS & Linux, pip on Windows. Either way it's a one-command install with no venv to create, activate, or reactivate in every new terminal.

macOS & Linux — uv

uv installs Plexavo into its own isolated environment automatically.

1. Install uv (if you don't already have it):

curl -LsSf https://astral.sh/uv/install.sh | sh

(Full options: uv installation docs.)

2. Install Plexavo:

uv tool install plexavo

That's it — plexavo is now on your PATH in every terminal, until you uninstall it. No activate step, ever.

Have an Anthropic API key and want AI-narrated explanations (see Cost — it's optional and costs a few cents per scan, not free)? Install with the [ai] extra instead — same package, just with the anthropic library included:

uv tool install "plexavo[ai]"

No key yet, or not sure? Skip it for now — the plain install is genuinely complete on its own. Add it later with:

uv tool install --reinstall "plexavo[ai]"

Just want to try it once without installing anything?

uvx plexavo scan --profile my-aws-profile

Updating or removing later:

uv tool upgrade plexavo
uv tool uninstall plexavo

Windows — pip

Install with pip and run the tool as py -m plexavo:

py -m pip install --user plexavo
py -m plexavo

py -m plexavo with no arguments opens the interactive menu, exactly like the plexavo command does on macOS/Linux — flags are only for scripting/CI.

Why not uv/pipx on Windows? Both work by putting a small generated plexavo.exe launcher on your PATH. That launcher is unsigned, and Windows Smart App Control blocks unsigned executables it doesn't recognise — so plexavo can fail to start with a "can't confirm who published" message. py -m plexavo calls Python directly and never touches that launcher, so it always works. (If your machine doesn't enforce Smart App Control, uv tool install plexavo works here too.)

AI-narrated explanations (see Cost):

py -m pip install --user "plexavo[ai]"

Updating or removing later:

py -m pip install --user --upgrade plexavo
py -m pip uninstall plexavo

Optional: type plexavo instead of py -m plexavo

Add a shortcut to your PowerShell profile once:

Add-Content $PROFILE 'function plexavo { py -m plexavo @args }'

Open a new terminal and plexavo then works just like it does on macOS/Linux.

Run a scan

plexavo scan --profile my-aws-profile --report-html report.html

(On Windows without the shortcut above: py -m plexavo scan --profile my-aws-profile --report-html report.html.)

No --profile? It uses your default profile / environment variables, same resolution order as the AWS CLI. No AI, no API key, no cost — and findings still come with free Next Step / Full Fix Detail guidance wherever a template exists (10 common check types); this alone is a complete, genuinely useful scan.

Want a full AI-written explanation for every finding instead (needs the [ai] install above)?

export ANTHROPIC_API_KEY="sk-ant-..."   # your own key, your own account
plexavo scan --profile my-aws-profile --explain --report-html report.html --report-pdf report.pdf
  • Drop --explain and findings still get free template remediation where available (no API key needed) and raw technical detail otherwise. --explain replaces that with a live AI narrative for every finding, including the templated ones.
  • Drop --report-html/--report-pdf to just see the console table.
  • --explain-limit N (default 25) caps how many findings get a live AI call when --explain is passed, as a safety rail against unexpectedly large real scans — it doesn't limit the free template remediation.
  • No ANTHROPIC_API_KEY set, or a call fails for any reason (invalid key, rate limit, network issue)? The scan and report are completely unaffected — that finding falls back to template/raw detail instead of an AI narrative, not an error. See Cost.

Alternative: pipx

Already use pipx on macOS/Linux? It works exactly the same way as uv — its own isolated environment, one global command, no venv:

pipx install plexavo
pipx install "plexavo[ai]"   # with AI-narrated explanations

On Windows, pipx has the same Smart App Control caveat as uv (see the Windows section above) — if the plexavo command is blocked, run py -m plexavo instead, or use the pip install.

Alternative: pip + venv

Want full isolation and prefer to manage the environment yourself? Modern Python (PEP 668) blocks a plain pip install outside a venv on many Linux and Homebrew setups, so create one first:

python -m venv plexavo-env
plexavo-env\Scripts\activate      # Windows
source plexavo-env/bin/activate   # Mac/Linux

pip install plexavo
pip install "plexavo[ai]"   # with AI-narrated explanations

You'll need to reactivate this venv (plexavo-env\Scripts\activate / source plexavo-env/bin/activate) every time you open a new terminal — deactivate exits it without uninstalling anything. On Windows, run python -m plexavo if the plexavo command is blocked by Smart App Control.

From source (for contributing, or trying an unreleased change)

git clone https://github.com/plexavo/plexavo.git
cd plexavo
uv pip install -e .   # or: pip install -e . (inside a venv)

Project structure

plexavo/
├── auth.py                    # local AWS credential resolution
├── principals.py               # enumerates IAM users/roles + their policies
├── findings.py                  # Finding data model, Severity enum
├── scoring.py                    # 0-100 score from a list of Findings
├── cli.py                         # `plexavo scan ...` entry point
├── __main__.py                      # lets `python -m plexavo` run the CLI
├── checks/
│   ├── iam.py                       # IAM-01 to IAM-06 (privilege escalation)
│   ├── iam_hygiene.py                # IAM-07 to IAM-14 (hygiene, cross-account trust)
│   ├── network.py                     # NET-01 to NET-04
│   ├── storage.py                      # STOR-19 to STOR-21
│   ├── encryption.py                    # ENC-29 to ENC-31
│   ├── logging.py                        # LOG-22 to LOG-25
│   └── usage.py                           # USE-26, USE-27
└── report/
    ├── html_report.py               # assembles findings into HTML via Jinja2
    ├── pdf.py                        # same data, rendered to PDF via fpdf2
    ├── ai_narration.py                # opt-in Claude narration + the 10 free templates
    ├── fonts/                          # bundled DejaVu Sans (PDF) + Geist (HTML) —
    │                                     both self-hosted, zero external requests
    └── templates/report.html.j2         # the HTML report template

examples/
└── quickstart-sandbox.tf       # one cheap, deliberately-public S3 bucket —
                                  try the scanner without pointing it at
                                  real infrastructure on day one

tests/
└── test_*.py                   # one per module, no AWS calls, run anytime:
                                  python tests/test_iam_offline.py, etc.

docs/
├── *-TEST-MATRIX.md            # one per category — the actual grading
│                                  record: what's verified, how, and any
│                                  stated limitation. Start here if you
│                                  want to know how much to trust a
│                                  given check.
├── COMPARISON.md               # honest, hands-on comparison against
│                                  Prowler, ScoutSuite, and PMapper
├── TECHNICAL-EXPLAINER.md      # implementation decisions and why
└── HOW-IT-WORKS.md

Cost

Detection is free (pure Python/boto3), always, regardless of anything else in this section. Every scan also gets free Next Step / Full Fix Detail remediation wherever one of 10 hand-written templates matches the finding type — no flag, no API key, zero cost, on by default.

Live AI only runs with --explain, and when it does it's used for every finding, including the 10 templated ones (a deliberate choice — "AI narration on" always means fully AI-written content, not a mix of template and AI), typically $0.01-0.02 per finding depending on answer length. A full scan with --explain on a real account is usually well under a dollar. This is your own ANTHROPIC_API_KEY, in your own Anthropic account — this project never sees your key, never embeds one of its own, and never calls the API on your behalf without you having set one. Not "free AI" — bring your own key, and a scan with it enabled costs a few cents.

Running the tests

pip install -e ".[dev]"
for f in tests/test_*.py; do python "$f"; done

Each is self-contained — fake AWS API responses, no real credentials or network calls needed. See docs/TEST-MATRIX.md for how these map to live-AWS verification.

Contributing

See CONTRIBUTING.md — in particular, the pattern for adding a new check.

Security

Found a vulnerability in the tool itself (not a misconfiguration in your own AWS account — that's the tool working correctly)? See SECURITY.md for a private reporting path.

License

AGPL-3.0 — see LICENSE. You can use, run, and modify this freely. If you run a modified version as a hosted service, you're required to publish those modifications too. This is deliberate: it's the specific protection against a well-resourced company taking this code and standing up a competing hosted product without ever contributing back.

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