Momentopnamen van modeltoestand

# evidence_preservation/model_snapshot.py
"""
Volledige vastlegging van de modeltoestand voor forensisch behoud.
"""
import hashlib
import json
import os
import shutil
from datetime import datetime
from dataclasses import dataclass, field
 
@dataclass
class ModelSnapshot:
    # Identificatie
    snapshot_id: str
    incident_id: str
    captured_by: str
    capture_timestamp: datetime
 
    # Modelidentiteit
    model_name: str
    model_version: str
    model_provider: str          # "self-hosted" of naam van de aanbieder
    model_registry_url: str
 
    # Modelartefacten (voor self-hosted modellen)
    weights_hash: str = ""
    weights_location: str = ""
    tokenizer_hash: str = ""
    tokenizer_location: str = ""
 
    # Configuratie
    system_prompt: str = ""
    system_prompt_hash: str = ""
    generation_parameters: dict = field(default_factory=dict)
    # temperature, top_p, max_tokens, enz.
 
    # Guardrails en filters
    guardrail_config: dict = field(default_factory=dict)
    guardrail_config_hash: str = ""
    content_policy: dict = field(default_factory=dict)
    content_policy_hash: str = ""
 
    # Tooldefinities (voor agentic systemen)
    tool_definitions: list = field(default_factory=list)
    tool_definitions_hash: str = ""
 
    # RAG-configuratie (indien van toepassing)
    rag_config: dict = field(default_factory=dict)
    embedding_model: str = ""
    vector_db_snapshot_location: str = ""
 
    # Gedragsfingerprint
    behavioral_fingerprint: dict = field(default_factory=dict)
 
    # Infrastructuurcontext
    deployment_manifest: dict = field(default_factory=dict)
    infrastructure_version: str = ""

def capture_self_hosted(model_path: str, config_path: str,
                         output_dir: str) -> ModelSnapshot:
    """
    Leg de volledige toestand van een self-hosted model vast.
    """
    snapshot = ModelSnapshot(
        snapshot_id=generate_id(),
        capture_timestamp=datetime.utcnow(),
        model_provider="self-hosted",
    )
 
    # Stap 1: Hash de modelgewichten (niet kopiëren — te groot)
    snapshot.weights_hash = hash_file(
        os.path.join(model_path, "model.safetensors")
    )
    snapshot.weights_location = model_path
 
    # Stap 2: Hash en kopieer de tokenizer
    tokenizer_path = os.path.join(model_path, "tokenizer.json")
    snapshot.tokenizer_hash = hash_file(tokenizer_path)
    shutil.copy2(tokenizer_path,
                  os.path.join(output_dir, "tokenizer.json"))
 
    # Stap 3: Leg de systeemprompt en configuratie vast
    with open(config_path) as f:
        config = json.load(f)
 
    snapshot.system_prompt = config.get("system_prompt", "")
    snapshot.system_prompt_hash = hashlib.sha256(
        snapshot.system_prompt.encode()
    ).hexdigest()
 
    snapshot.generation_parameters = {
        "temperature": config.get("temperature"),
        "top_p": config.get("top_p"),
        "max_tokens": config.get("max_tokens"),
        "stop_sequences": config.get("stop_sequences", []),
    }
 
    # Stap 4: Leg de guardrail-configuratie vast
    guardrail_path = config.get("guardrail_config_path")
    if guardrail_path and os.path.exists(guardrail_path):
        with open(guardrail_path) as f:
            snapshot.guardrail_config = json.load(f)
        snapshot.guardrail_config_hash = hash_file(guardrail_path)
        shutil.copy2(guardrail_path,
                      os.path.join(output_dir, "guardrails.json"))
 
    # Stap 5: Leg de tooldefinities vast
    tool_path = config.get("tool_definitions_path")
    if tool_path and os.path.exists(tool_path):
        with open(tool_path) as f:
            snapshot.tool_definitions = json.load(f)
        snapshot.tool_definitions_hash = hash_file(tool_path)
 
    # Stap 6: Schrijf het manifest van de momentopname
    manifest_path = os.path.join(output_dir, "snapshot_manifest.json")
    with open(manifest_path, "w") as f:
        json.dump(snapshot.__dict__, f, indent=2, default=str)
 
    return snapshot

def capture_api_hosted(api_config: dict,
                        output_dir: str) -> ModelSnapshot:
    """
    Leg de toestand van een API-gehoste modeldeployment vast.
    Kan geen gewichten vastleggen, maar legt al het andere vast.
    """
    snapshot = ModelSnapshot(
        snapshot_id=generate_id(),
        capture_timestamp=datetime.utcnow(),
        model_provider=api_config["provider"],
        model_name=api_config["model_name"],
        model_version=api_config.get("model_version", "unknown"),
    )
 
    # Voor API-modellen kunnen we geen gewichten hashen, maar moeten we
    # de exacte model-identifier registreren
    snapshot.weights_hash = "N/A — API-hosted model"
    snapshot.weights_location = (
        f"{api_config['provider']}:{api_config['model_name']}"
    )
 
    # Leg de configuratie op applicatieniveau vast
    snapshot.system_prompt = api_config.get("system_prompt", "")
    snapshot.system_prompt_hash = hashlib.sha256(
        snapshot.system_prompt.encode()
    ).hexdigest()
 
    snapshot.generation_parameters = {
        k: v for k, v in api_config.items()
        if k in ["temperature", "top_p", "max_tokens",
                  "frequency_penalty", "presence_penalty"]
    }
 
    return snapshot

# evidence_preservation/behavioral_fingerprint.py
"""
Gedragsfingerprinting voor het vergelijken van modeltoestanden.
"""
import numpy as np
from collections import Counter
 
class BehavioralFingerprint:
    def __init__(self, model_endpoint):
        self.endpoint = model_endpoint
 
    def generate_fingerprint(self, probe_suite: dict,
                              repetitions: int = 5) -> dict:
        """
        Genereer een gedragsfingerprint door gestandaardiseerde probes
        uit te voeren en de reactiepatronen te karakteriseren.
        """
        fingerprint = {
            "timestamp": datetime.utcnow().isoformat(),
            "model_version": self.endpoint.version,
            "probe_results": {},
            "aggregate_metrics": {},
        }
 
        all_response_lengths = []
        all_refusal_rates = {}
 
        for category, prompts in probe_suite.items():
            category_results = []
 
            for prompt in prompts:
                prompt_results = []
                refusals = 0
 
                for rep in range(repetitions):
                    response = self.endpoint.generate(prompt)
                    is_refusal = self._is_refusal(response.text)
 
                    prompt_results.append({
                        "response_length": len(response.text),
                        "token_count": response.token_count,
                        "is_refusal": is_refusal,
                        "response_hash": hashlib.sha256(
                            response.text.encode()
                        ).hexdigest()[:16],
                    })
 
                    all_response_lengths.append(len(response.text))
                    if is_refusal:
                        refusals += 1
 
                refusal_rate = refusals / repetitions
                all_refusal_rates[f"{category}:{prompt[:50]}"] = refusal_rate
 
                category_results.append({
                    "prompt": prompt,
                    "refusal_rate": refusal_rate,
                    "avg_response_length": np.mean(
                        [r["response_length"] for r in prompt_results]
                    ),
                    "response_variance": np.var(
                        [r["response_length"] for r in prompt_results]
                    ),
                    "unique_responses": len(set(
                        r["response_hash"] for r in prompt_results
                    )),
                })
 
            fingerprint["probe_results"][category] = category_results
 
        # Geaggregeerde metrieken
        fingerprint["aggregate_metrics"] = {
            "overall_refusal_rate": np.mean(
                list(all_refusal_rates.values())
            ),
            "avg_response_length": np.mean(all_response_lengths),
            "response_length_std": np.std(all_response_lengths),
            "safety_probe_refusal_rate": np.mean([
                v for k, v in all_refusal_rates.items()
                if "safety" in k
            ]) if any("safety" in k for k in all_refusal_rates) else None,
        }
 
        return fingerprint
 
    def _is_refusal(self, response_text: str) -> bool:
        """Heuristische controle op een weigering van het model."""
        refusal_indicators = [
            "i cannot", "i can't", "i'm unable to",
            "i am unable to", "i will not", "i won't",
            "i'm not able to", "as an ai",
            "against my guidelines", "not appropriate",
        ]
        lower = response_text.lower()
        return any(indicator in lower
                   for indicator in refusal_indicators)

def compare_fingerprints(pre_incident: dict,
                          post_incident: dict) -> dict:
    """
    Vergelijk twee gedragsfingerprints om veranderingen te identificeren.
    """
    changes = {
        "metric_changes": {},
        "category_changes": {},
        "significant_changes": [],
    }
 
    # Vergelijk geaggregeerde metrieken
    pre_metrics = pre_incident["aggregate_metrics"]
    post_metrics = post_incident["aggregate_metrics"]
 
    for metric in pre_metrics:
        if pre_metrics[metric] is None or post_metrics.get(metric) is None:
            continue
        pre_val = pre_metrics[metric]
        post_val = post_metrics[metric]
        delta = post_val - pre_val
 
        changes["metric_changes"][metric] = {
            "pre": pre_val,
            "post": post_val,
            "delta": delta,
            "percent_change": (delta / pre_val * 100)
                              if pre_val != 0 else float("inf"),
        }
 
        # Markeer significante veranderingen
        if abs(delta / (pre_val + 1e-10)) > 0.1:  # >10% verandering
            changes["significant_changes"].append({
                "metric": metric,
                "change": f"{delta / (pre_val + 1e-10) * 100:.1f}%",
                "direction": "increase" if delta > 0 else "decrease",
            })
 
    return changes

# evidence_preservation/integrity_check.py
"""
Verifieer de integriteit van modelartefacten tegen bekende, goede hashes.
"""
 
class ModelIntegrityChecker:
    def __init__(self, registry):
        self.registry = registry
 
    def verify_deployed_model(self, deployment_path: str,
                                expected_version: str) -> dict:
        """
        Verifieer dat de uitgerolde modelartefacten overeenkomen met
        de geregistreerde versie.
        """
        expected = self.registry.get_version(expected_version)
        results = {"verified": True, "checks": []}
 
        # Controleer elk artefact
        artifacts_to_check = [
            ("model weights", "model.safetensors",
             expected.weights_hash),
            ("tokenizer", "tokenizer.json",
             expected.tokenizer_hash),
            ("config", "config.json",
             expected.config_hash),
        ]
 
        for name, filename, expected_hash in artifacts_to_check:
            filepath = os.path.join(deployment_path, filename)
            if not os.path.exists(filepath):
                results["checks"].append({
                    "artifact": name,
                    "status": "MISSING",
                    "expected_hash": expected_hash,
                })
                results["verified"] = False
                continue
 
            actual_hash = hash_file(filepath)
            match = actual_hash == expected_hash
 
            results["checks"].append({
                "artifact": name,
                "status": "MATCH" if match else "MISMATCH",
                "expected_hash": expected_hash,
                "actual_hash": actual_hash,
            })
 
            if not match:
                results["verified"] = False
 
        return results