[ CCS Status: ⚠️ NON_COMPLIANT ]

The Industry Standard for
LLM Output Verification

Don't guess if your agent is hallucinating.
Verify it against the Cryptographic Compliance Standard.

$ pip install correctover
Read Specification View CCS Protocol

CCS Compliance Matrix

20,299 real production responses. 7 models. 6 providers. One standard.

Provider Model Score D1 D2 D3 D4 D5 Certification
Meta Llama-3.1-70B 70% 100% 75% 33% 67% 67% ⚠️ CONDITIONAL
OpenAI GPT-OSS-120B 35% 67% 12% 67% 33% 33% ❌ NON-COMPLIANT
Microsoft Phi-3.5-MoE 0% 0% 0% 0% 0% 0% 💀 BROKEN
Microsoft Phi-4-Multimodal 0% 0% 0% 0% 0% 0% 💀 BROKEN
Databricks DBRX 0% 0% 0% 0% 0% 0% 💀 BROKEN
IBM Granite-34B 0% 0% 0% 0% 0% 0% 💀 BROKEN
Google Gemma-3-12B 0% 0% 0% 0% 0% 0% 💀 BROKEN
0 / 7
Zero models achieved CCS certification.
85.7% cannot be used in production agent systems.

CCS Compliance Scanner

Run the full 5-dimension verification. Get your compliance grade.

ccs_scanner — compliance scan
$ python -m correctover.scan --provider openai

══════════════════════════════════════════════════════ CCS v1.0 COMPLIANCE REPORT OPENAI / gpt-oss-120b ══════════════════════════════════════════════════════
OVERALL SCORE: 35.0% CERTIFICATION: ❌ NON_COMPLIANT
── 5-Dimension Verification ──────────────────────
D1 Schema Compliance █████████████░░░░░░░ 66.7% D2 Arithmetic Integrity ██░░░░░░░░░░░░░░░░ 12.5% └─ SILENT_CORRUPTION: 2+3=6 returned with HTTP 200 D3 Factual Grounding █████████████░░░░░░░ 66.7% └─ HALLUCINATION: Fabricated 2 of 3 paper citations D4 Temporal Consistency ██████░░░░░░░░░░░░░░ 33.3% D5 Output Reproducibility ██████░░░░░░░░░░░░░░ 33.3% └─ NON_DETERMINISTIC: Arithmetic varies between runs
── Verdict ───────────────────────────────────────
❌ NON-COMPLIANT: This model CANNOT be used in production agent systems without CCS verification layer.
View Scanner Source

5 Verification Dimensions

Required(τ) ⊆ Supported(τ) — the invariant that defines compliance.

D1

Schema

Does output match required structure?

D2

Arithmetic

Does 2+3=5? Silent corruption detection.

D3

Factual

Are references real or hallucinated?

D4

Temporal

Is the model time-aware? Stale knowledge.

D5

Reproducibility

Same input → same output? Determinism.

Level Criteria Production Ready
✅ CERTIFIED ≥95% across all 5 dimensions Yes
⚠️ CONDITIONAL 70-95%, architectural limitations Only with CCS runtime
❌ NON-COMPLIANT <70%, schema violations No
💀 BROKEN 0%, fundamental failures Absolutely not

30-Second Integration

One install. Three lines. Production-ready verification.

from correctover import CCSValidator validator = CCSValidator( required_fields=["result", "action", "timestamp"], forbidden_fields=["error", "stack_trace"], enable_integrity=True ) result = validator.validate(llm_output, trace_id="agent-001") if not result.is_valid: raise IntegrityViolation(result.errors) # That's it. Your pipeline is now CCS-compliant.

The 5 Components

CCS-Core

Schema validation. Required/Supported/Forbidden fields.

CCS-Compliance

HIPAA, GDPR, SOC2 regulatory mapping.

CCS-Cost-Flow

Token budget enforcement.

CCS-Integrity

HMAC-based output binding.

CCS-Audit

Immutable audit trails.

Join the Integrity Movement

Q3 Industry Reliability Benchmark — July 11, 2026

Frameworks that have not adopted CCS verification protocols will be categorized by their technical risk level.

Documentation & Full Audit Methodology Get Your Compliance Badge