LitmusAI

Free, deterministic Article 5 screener for the EU AI Act.

LitmusAI 1.1.3 ships with the AiExponent reference ruleset ruleset-2024-1689-v1.1.1 (UNREVIEWED: internal panel authored, no external lawyer review). Apache 2.0, AS IS.

The package's CLI surface, JSON/SARIF schema, and BYO-ruleset contract are stable for production integration. The reference ruleset has been authored and reviewed by an internal AiExponent panel (six engineering + governance roles) but has not been reviewed by a qualified EU AI Act practising lawyer. Every screening report carries this disclosure prominently. Customers who require lawyer-reviewed output can supply their own signed ruleset via the BYO mechanism; see docs/ruleset-authoring.md.

The legal-review status is not tied to a version number. litmus --version prints ruleset_legal_status: REVIEWED when the active ruleset carries a signature block, and UNREVIEWED when it does not. LitmusAI checks that the block is well formed; it does not verify the signature cryptographically or check who the signer is, so that line records what the ruleset author declared, not proof of a qualified legal review. Tracked under Legal review status below.

The Problem

On February 2, 2025, Article 5 of the EU AI Act (Regulation (EU) 2024/1689) entered into active legal enforcement across all 27 EU member states.

Placing a prohibited AI practice on the market or putting it into service is strictly illegal and carries statutory administrative fines of up to €35,000,000 or 7% of total worldwide annual turnover.

Most engineering and product teams do not have an in-house EU AI Act counsel to review every model prompt, feature iteration, or system deployment. LitmusAI answers one critical question in your terminal or CI pipeline before you deploy:

"Is this AI practice prohibited under Article 5?"

Screen your system against all ten statutory categories of Article 5(1): the original eight, points (a) to (h), and the two inserted by Regulation (EU) 2026/1744, points (ba) and (bb), which apply from 2 December 2026. Receive a per-category Red / Amber / Clear verdict with exact legal citations, confidence levels, and concrete remediation advice, in under 60 seconds, completely offline, and without uploading sensitive system descriptions to any third-party server.

Built by AI Exponent LLC. Apache 2.0. Runs entirely offline after pip install.

Quick Start

pip install litmus-screener   # the brand is "LitmusAI"; the PyPI distribution is "litmus-screener"

# 1. Quick screen from a plain-text prompt
litmus screen --describe "a chatbot for mental health support for teenagers"

# 2. Or initialize a structured declaration for your system
litmus init                                         # creates system.yaml starter template
litmus screen system.yaml --output report.json      # full ten-category evaluation

# 3. Export audit-ready reports for legal or security teams
litmus export report.json -o report.sarif --format sarif

Why LitmusAI

System Architecture

LitmusAI operates as a deterministic, offline static-analysis pipeline:

graph TB
    subgraph Inputs ["1. Input Specifications"]
        INP1["Prompt Description<br/><code>litmus screen --describe '...'</code>"]
        INP2["Declarative YAML<br/><code>system.yaml</code>"]
        INP3["CI/CD Gate<br/><code>GitHub Actions / Pre-commit</code>"]
    end

    subgraph Core ["2. Deterministic Screening Engine"]
        AST["Safe AST Evaluator<br/><em>(Constrained expressions, zero eval, zero Python execution)</em>"]
        RULE["Article 5 Reference Ruleset<br/><em>(28 rules covering 5.1.a to 5.1.h, 5.1.ba and 5.1.bb)</em>"]
        BYO["Bring-Your-Own Ruleset<br/><em>(Optional lawyer-signed JSON via litmus use-ruleset)</em>"]
    end

    subgraph Integrity ["3. Verification & Scoring"]
        CONF["Confidence Scorer<br/><em>(Conservative: Amber preferred on ambiguity)</em>"]
        HASH["RFC 8785 Canonical Digest<br/><em>(SHA-256 tamper-evident input hash)</em>"]
    end

    subgraph Outputs ["4. Audit-Ready Artifacts"]
        OUT1["Rich Terminal UI<br/><em>(Color-coded Red / Amber / Clear table)</em>"]
        OUT2["SARIF 2.1.0 Report<br/><em>(GitHub Security tab & GitLab SAST)</em>"]
        OUT3["JSON / Markdown<br/><em>(Reports for legal and security review)</em>"]
    end

    INP1 & INP2 & INP3 --> AST
    RULE & BYO --> AST
    AST --> CONF
    CONF --> HASH
    HASH --> OUT1 & OUT2 & OUT3

    style Inputs fill:#F5F4EF,stroke:#E4E2DC,color:#0F1419
    style Core fill:#E6F4F1,stroke:#0D5463,color:#0D5463
    style Integrity fill:#FAF0E6,stroke:#B68A2E,color:#0F1419
    style Outputs fill:#F5F4EF,stroke:#E4E2DC,color:#0F1419

What LitmusAI Does

• All ten Article 5 points: The reference ruleset has rules for every prohibited practice under Article 5(1), points (a) to (h) plus (ba) and (bb) from Regulation (EU) 2026/1744. A report run before 2 December 2026 says that the two new points apply from that date. How well each point is detected is measured, and limited; see Known limits.
• Deterministic by Design: Same input + same ruleset = identical output, guaranteed across platforms.
• Zero Network Calls (NFR-8): 100% offline execution. No telemetry, no telemetry pings, no crash reports. Your architecture and prompts never leave your local machine (enforced in CI via pytest-socket).
• Cryptographic Tamper-Evidence: Generates a canonical RFC 8785 SHA-256 digest on every report. Verify report integrity anytime via litmus verify <report>.
• Bring-Your-Own-Ruleset (BYO): Legal counsel can author, sign, and distribute custom rulesets. Once activated (litmus use-ruleset), reports show (SIGNED by: Counsel) rather than UNREVIEWED in the provenance line.
• Multi-Format Export: Native support for rich console output, machine-readable JSON, Markdown documentation, and SARIF 2.1.0.

Article 5 Categories Covered

Known limits

LitmusAI screens what a system.yaml declares, and a rule can only use a field that exists. An independent test, run on 2 November 2026 by a tester who never saw the rules, screened 175 worked examples from the European Commission's Guidelines on prohibited AI practices (C(2025) 5052) and the recitals of Regulation (EU) 2026/1744 with litmus-screener 1.1.2. The cases, the expected answers written before the run, the scripts and the full scorecard are in verification/, and anyone can re-run them with verification/run.sh.

What that means in practice:

• Points (a) and (b) are not detected from their legal elements. There is no field for manipulative, deceptive or subliminal techniques, for significant harm, or for exploiting a disability or older age. Screen these points yourself.
• Exceptions and exclusions are not modelled for points (d), (e), (f) and (h): human assessment on objective facts, targeted collection, medical or safety purposes, authorised law-enforcement use. Most false alarms come from these gaps, so a RED on those points needs a reviewer.
• deployment_context.workplace and education describe where the system is used. A system used on customers or patients in a workplace still reads RED on point (f).
• The quick screen (--describe) infers fields from keywords and misses more: 27 of 51 expected prohibitions came back clear.

A release that adds the missing fields will be re-tested the same way, and the result published next to this one.

Interactive Artifact Previews

# LitmusAI System Description
# Edit the fields below to describe your AI system.
# Then run: litmus screen system.yaml

name: "My AI System"
version: "1.0.0"
provider: "My Organisation"
deployer: "My Organisation"
purpose: "Describe what the system does in one sentence."
system_description: |
  A more detailed description of the system: what inputs it takes,
  what outputs it produces, and how those outputs are used.

deployment_jurisdictions:
  - EU

output_consumed_in_eu: true

subject_population:
  categories:
    - general_public
  notes: null

inputs:
  biometric: false
  facial_images: false
  emotional_state_inference: false
  behaviour_history: false
  social_behaviour_data: false        # conduct outside the service: social media, associations
  scraped_internet_data: false
  freetext_prompts: false
  other: null

outputs:
  individual_scores: false
  behaviour_predictions: false
  criminal_risk_predictions: false
  emotion_inferences: false
  sensitive_attribute_classifications: false
  freetext_generations: false
  detrimental_treatment_unrelated_context: false   # score used against the person elsewhere
  other: null

deployment_context:
  workplace: false
  education: false
  public_space: false
  real_time_operation: false
  law_enforcement_use: false
  healthcare: false
  financial_services: false
  other: null

# Article 5(1)(ba) and (bb), inserted by Regulation (EU) 2026/1744 and
# applicable from 2 December 2026. Leave everything false for a system
# that does not generate or manipulate realistic images, video or audio.
generative_media:
  realistic_media_generation: false   # realistic images, video, audio or similar material
  identifiable_persons: false         # outputs can depict identifiable real people
  intimate_depictions_possible: false # intimate parts or sexual activity, without major modification
  intimate_depictions_intended: false # that output is the system's purpose
  minors_depictions_possible: false   # outputs can depict children in such material
  manipulation_within_1b_only: false  # edits that neither increase exposure nor change the activity
  safeguards:                         # each one: in place, in force and verifiable
    consent_demonstrable: false       # explicit consent of each depicted person, collected and provable
    intimate_output_prevention: false # filters, refusal training, guardrails against intimate outputs
    csam_prevention: false            # measures against child sexual abuse material
    misuse_correction: false          # abuse detection, notice and action, follow-up on circumvention
    documented: false                 # the measures above are written down and testable

mitigations: []

metadata:
  last_reviewed: null
  owner: null
  reviewed_by: null

{
  "$schema": "https://raw.githubusercontent.com/oasis-tcs/sarif-spec/main/sarif-2.1/schema/sarif-schema-2.1.0.json",
  "version": "2.1.0",
  "runs": [
    {
      "tool": {
        "driver": {
          "name": "LitmusAI",
          "version": "1.1.3",
          "informationUri": "https://aiexponent.com/products/litmusai",
          "rules": [
            {
              "id": "litmusai/5.1.b",
              "name": "Vulnerability Exploitation",
              "shortDescription": {
                "text": "Article 5(1)(b) Exploitation of age, disability, or specific social vulnerability"
              },
              "helpUri": "https://aiexponent.com/eu-ai-act/article-5"
            }
          ]
        }
      },
      "results": [
        {
          "ruleId": "litmusai/5.1.b",
          "level": "warning",
          "message": {
            "text": "Conversational agent targets minors experiencing psychological distress without clinician escalation safeguards."
          }
        }
      ]
    }
  ]
}

CI/CD Integration & Exit Codes

Add LitmusAI to your GitHub Actions workflow as an automated pre-merge compliance gate:

# .github/workflows/article5.yml
name: EU AI Act Article 5 Screening
on: [pull_request, push]

jobs:
  article5-gate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v6
      - uses: aiexponent/litmusai/.github/actions/litmusai-screen@v1.1.3   # pin a release tag
        with:
          path: system.yaml
          fail-on: amber       # fail CI if overall verdict is RED or AMBER
          output-format: sarif

Exit Code Contract

LitmusAI implements deterministic UNIX exit codes for scripting and CI gates:

CLI Command Reference

Documentation

• Getting Started Guide
• Article 5 Statutory Coverage
• Bring Your Own Ruleset (BYO) Guide
• CI/CD Integration Guide

Important Disclaimers

Legal Review Status

UNREVIEWED REFERENCE RULESET

The default LitmusAI ruleset (ruleset-2024-1689-v1.1.1) is a good-faith engineering interpretation of Article 5, as amended by Regulation (EU) 2026/1744, authored by AiExponent's internal compliance panel. It has not been reviewed or signed by a qualified EU AI Act lawyer and is not legal advice. The panel's review records ship with the package under litmusai/_data/ruleset/.

The package version (litmusai 1.1.3) reflects API and CLI stability. The legal-review status rides on the ruleset itself: litmus --version prints ruleset_legal_status: REVIEWED when the active ruleset carries a signature block and UNREVIEWED when it does not. That check is structural. LitmusAI does not verify the signature cryptographically and cannot vouch for the signer's qualifications, so the line reports what the ruleset's author declared; whether the review behind it was qualified is for you to confirm with the signer. No version number of the reference ruleset implies an external review.

If your organisation needs a lawyer-signed ruleset today, see docs/ruleset-authoring.md for the BYO-ruleset path. A complete dummy-signed example ships in tests/fixtures/rulesets/acme-corp-signed-v1.0.json.

Every screening is a screening, not a certification. Not legal advice. Not a notified body.

Privacy & Zero-Network Guarantee

LitmusAI makes zero network calls during screening. No telemetry, no usage analytics, no error reporting. Your prompts, proprietary architecture, and system declarations never leave your local machine.

Enforced in automated CI via pytest-socket --disable-socket (NFR-8).

License

Apache 2.0; see LICENSE.

Part of the AiExponent open-source AI governance toolchain:
litmusai (Art. 5) · license-compliance-checker (Art. 53) · rag-benchmarking (Art. 15) · riskforge (Art. 9) · agentic-document-analyser (Art. 9 / Annex IV)