Skip to main content

meok-ai-psych-vuln-audit-mcp

EU AI Act Article 5(1)(f) gambling-vertical compliance audit MCP.

The first MCP that audits AI systems for the EU AI Act's prohibition on "the use of AI systems to exploit psychological vulnerabilities of specific groups (including children)" — as applied to the gambling vertical (online casinos, sportsbooks, lotteries).

Added to the prohibited list by the Digital Omnibus amendments in May 2026, Article 5(1)(f) bans AI that exploits known cognitive or psychological weaknesses. In the gambling vertical, this is exactly what bonus personalisation, loss-chasing detection bypass, and AI-driven push notifications do.

This MCP is the structured, auditable, cryptographically signed evidence layer: a regulator can take a signed audit report from this tool and verify it offline with no phone-home.

Installation

pip install meok-ai-psych-vuln-audit-mcp

Tools (4)

Tool Purpose
audit_player_intervention(player_action) Audit a single AI-driven player intervention (push, bonus, pop-up) against the 12 gambling-AI risk patterns.
scan_marketing_copy(copy, target_segment) Scan marketing copy targeting a player segment for FOMO, loss-framing, minor-targeting, and other Art 5(1)(f) triggers.
classify_ai_system(ai_system) Classify an AI system's purpose + training data + decision points for Art 5(1)(f) risk class.
generate_audit_report(operator_id, audit_period, interventions) Produce a regulator-ready, Ed25519-signed audit report over a list of AI interventions.

All four tools return a JSON envelope:

{
  "status": "PASS|REVIEW|FAIL",
  "triggered_patterns": [...],
  "severity_score": 0.0,
  "recommendations": [...],
  "signature": "<128 hex chars>"
}

The 12 Gambling-AI Risk Patterns

Each pattern is a structured entry in GAMBLING_RISK_PATTERNS with id, name, severity, evidence_examples, mitigation_pattern, eu_ai_act_article_ref, uk_lccp_ref, and a test_input that triggers it.

  1. loss_chasing_detection_bypass — AI doesn't flag a player chasing losses
  2. vulnerable_player_targeting — bonus offers to recently-deposited-but-losing players
  3. minor_appearance_targeting — cartoon graphics + AI copy for under-25 demos
  4. addiction_mimicry — variable-ratio reward timing in push notifications
  5. fomo_generation — "5 others are playing now" with no provenance
  6. near_miss_obfuscation — slot results framed as "almost won"
  7. deposit_limit_evasion — split UI to make limit-setting harder
  8. session_extension_manipulation — popups during losing streaks
  9. age_verification_circumvention — accepting partial KYC
  10. self_exclusion_bypass — multi-account creation not flagged
  11. ai_chatbot_empathy_exploitation — chatbots bonding to extend sessions
  12. personalised_loss_framing — losses framed as "investments"

Cryptography

Ed25519 signed (via the cryptography library). The signature is over canonicaljson.dumps(payload, sort_keys=True, separators=(",",":")).hexdigest() and the canonical payload, output as 128 hex chars (64 bytes).

The demo private key is bundled for the test/demo flow. In production this is replaced with the meok-compliance-gateway KMS.

License

MIT — Copyright (c) 2026 MEOK AI Labs CSOAI LTD.

mcp-name: io.github.CSOAI-ORG/meok-ai-psych-vuln-audit-mcp

Release files for meok-ai-psych-vuln-audit-mcp 0.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for meok-ai-psych-vuln-audit-mcp 0.1.2
File Size Uploaded
meok_ai_psych_vuln_audit_mcp-0.1.2.tar.gz 21.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for meok-ai-psych-vuln-audit-mcp 0.1.2
File Interpreter ABI Platform
meok_ai_psych_vuln_audit_mcp-0.1.2-py3-none-any.whl Python 3 none any Details

Total release size: 40.9 kB

Release files / meok_ai_psych_vuln_audit_mcp-0.1.2.tar.gz

Download URL meok_ai_psych_vuln_audit_mcp-0.1.2.tar.gz
Size 21.9 kB
Tags Source
SHA-256 checksum
How to use checksums
b5e69c6f39e8742b8490ee63bd273a33a59a35bbc2c48c5f46bbe642d28e500b
BLAKE2b-256 checksum
How to use checksums
e33890ccdcca337cda1960439f2b457885d99c29f428fea6d8688af10d6204ce
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.10.3 {"installer":{"name":"uv","version":"0.10.3","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

Release files / meok_ai_psych_vuln_audit_mcp-0.1.2-py3-none-any.whl

Download URL meok_ai_psych_vuln_audit_mcp-0.1.2-py3-none-any.whl
Size 19.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
3f2f6ef8c3ab49cb27662ad0db20a09ccecd4d6fb703aaae9fb9485dfcc5aa6f
BLAKE2b-256 checksum
How to use checksums
723655a29395a2e8414dfb91eccb5fd74522fb2753f081bf84e514e14788e015
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.10.3 {"installer":{"name":"uv","version":"0.10.3","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

Release history Release notifications | RSS feed

This release

0.1.2 This release

2 release files

0.1.1

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page