August 2026 is a real EU AI Act planning checkpoint for many teams. Use the free scan now, and request baseline review if security, procurement, or launch pressure is already active.

AI Framework Security Scores

Saved snapshot scans of 7 major open-source AI/ML frameworks show comparative enforcement maturity, context hygiene, and automation readiness. The average saved score is 29/100. None have L5 enforcement hooks.

Boundary Truth

See what this frameworks page shows and what it cannot know

These three zones separate saved ecosystem context from the next repo-level action and from limits that should not be mistaken for fresh findings.

Shown On This Page

Saved public-framework comparison

  • The table and cards preserve saved snapshot scores across 7 public frameworks for side-by-side comparison.
  • The portfolio average on this page is 29/100 for the saved assessment set shown here.
  • This page shows saved comparison context only, not current repo findings for a visitor's own codebase.

Next Step

Use the free scan for current repo findings

  • Use this page to choose which public framework evidence set is most relevant to your situation.
  • Run the free repo scan when you need current findings on your own repository instead of a saved ecosystem comparison.
  • Treat the baseline sprint as the first paid move only after a repo-level signal confirms a real gap.

Limit

Helpful explanation, not new findings

  • These snapshots are point-in-time saved scans rather than live default-branch reads.
  • They do not show current enforcement, owner mapping, or delivery status inside your repository.
  • The surrounding copy explains the comparison and next step, but it is not a new repo read beyond the saved data shown here.
FrameworkSaved ScoreSaved GradeStars
Transformers45/100C140,000+
LangChain40/100C100,000+
Django29/100D80,000+
FastAPI29/100D80,000+
Pydantic29/100D22,000+
scikit-learn18/100F60,000+
CrewAI13/100F25,000+

Transformers

C
45/100

The largest AI framework shows governance awareness but enforcement has not kept pace. Saved comparison context only; run the free scan for current repo findings.

Enforcement Maturity: 35Context Hygiene: 60Automation Readiness: 46
View findings →

LangChain

C
40/100

Early governance signals exist but zero enforcement hooks leave 100K-star framework exposed. Saved comparison context only; run the free scan for current repo findings.

Enforcement Maturity: 26Context Hygiene: 75Automation Readiness: 26
View findings →

Django

D
29/100

The most deployed Python web framework has 1,995 test files but zero enforcement hooks. Saved comparison context only; run the free scan for current repo findings.

Enforcement Maturity: 35Context Hygiene: 10Automation Readiness: 42
View findings →

FastAPI

D
29/100

Strong test coverage undermined by zero enforcement hooks and no AI agent instructions. Saved comparison context only; run the free scan for current repo findings.

Enforcement Maturity: 35Context Hygiene: 10Automation Readiness: 42
View findings →

Pydantic

D
29/100

The data validation library underpinning FastAPI and LangChain has zero enforcement hooks. Saved comparison context only; run the free scan for current repo findings.

Enforcement Maturity: 35Context Hygiene: 10Automation Readiness: 42
View findings →

scikit-learn

F
18/100

The foundational ML library has zero secrets but no structural enforcement. Saved comparison context only; run the free scan for current repo findings.

Enforcement Maturity: 20Context Hygiene: 10Automation Readiness: 26
View findings →

CrewAI

F
13/100

The leading multi-agent AI framework scores lowest in our governance portfolio. Saved comparison context only; run the free scan for current repo findings.

Enforcement Maturity: 10Context Hygiene: 10Automation Readiness: 22
View findings →

Key Takeaways

These takeaways summarize saved public-framework snapshots, not live findings about your repository today.

  • Zero L5 hooks across these 7 saved framework snapshots. In the captured public scans, not a single project showed pre-commit hooks or Claude Code hooks enforcing governance before code entered the repository.
  • Average saved AI security score: 29/100. Even the highest-scoring saved framework snapshot (Transformers, 45/100) still shows critical enforcement gaps.
  • Tests are not AI security proof by themselves. In this saved comparison set, Django has 1,995 test files but scores 29/100 because tests validate correctness, not enforcement posture.
  • Saved EU AI Act readiness averages about 19%. These snapshots suggest organizations using these frameworks will still need their own compliance layer before enforcement begins on August 2, 2026.

Next-Step Path

Match the next move to what you already have

These framework pages are saved comparison context. The free scan is the first current-state check for your repo. When the signal is real, the baseline sprint is the first paid move, and its request page reviews fit before delivery starts. Monitoring uses that same review path only after baseline work exists.

Current Page State

Saved framework comparison only

This page preserves saved comparison context across these public frameworks. It does not tell you what your repo looks like today or whether a paid engagement fits yet.

Right Next Move

Run the free scan on your repo

That is the first current-state signal for your own repository. Move to baseline sprint only after repo-level signal confirms a real gap, and keep monitoring for after baseline work exists.

Plain Next-Step Path

From this saved frameworks page, the next step is the free scan for your own repository. Request the baseline sprint only after a repo-level signal confirms a real gap, and keep monitoring for after baseline work exists.

1. Free Scan

Use when you need current repo findings

Start Here

Use the free scan when this framework comparison makes you ask, "what does our repo look like right now?" It gives fresh public-repo findings instead of another saved example.

This page only gives saved framework evidence, so the free scan is the first current-state check for your repo.

Start here when a framework score is useful context but not current enough to act on.

2. Baseline Sprint

Use when the signal is real and needs a fix plan

After Repo Proof

Use the baseline sprint after the free scan or an equivalent repo signal confirms the gap and you need a prioritized remediation order.

Keep this for after your own scan or equivalent repo signal confirms a real gap that needs a fix order.

This is the first paid move. The request page checks fit so current repo signal can turn into a concrete fix path before delivery starts.

3. Monitor

Use only after baseline work exists

After Baseline

Use the monitor only after baseline work is underway or complete. It is the continuity layer for drift review, not the first move from a saved framework comparison.

Monitoring is continuity work only after baseline enforcement exists, not the first move from a saved framework page.

If all you have is comparative framework context, skip this for now and start with the free scan.