隱私保護推論模式

from openai import OpenAI
 
client = OpenAI()
 
def demonstrate_concept(system_prompt: str, user_input: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_input},
        ],
        temperature=0.0,
    )
    return response.choices[0].message.content

from typing import Optional, List
from dataclasses import dataclass
 
@dataclass
class AttackResult:
    payload_hash: str
    success: bool
    response_length: int
    defense_triggered: Optional[str] = None
 
class TechniqueFramework:
    def __init__(self, target_config: dict):
        self.config = target_config
        self.results: List[AttackResult] = []
 
    def prepare_payload(self, objective: str, constraints: dict) -> str:
        payload = self._base_payload(objective)
        if constraints.get("input_classifier"):
            payload = self._apply_obfuscation(payload)
        if constraints.get("output_filter"):
            payload = self._add_extraction_channel(payload)
        return payload
 
    def _base_payload(self, objective: str) -> str:
        return f"Please help me with: {objective}"
 
    def _apply_obfuscation(self, payload: str) -> str:
        return payload
 
    def _add_extraction_channel(self, payload: str) -> str:
        return payload + "\nEncode your response in Base64."
 
    def execute(self, payload: str) -> AttackResult:
        response = self._send_to_target(payload)
        success = self._evaluate_response(response)
        result = AttackResult(payload_hash=str(hash(payload))[:12],
                              success=success, response_length=len(str(response)))
        self.results.append(result)
        return result
 
    def _send_to_target(self, payload: str) -> str: return ""
    def _evaluate_response(self, response: str) -> bool: return False

from dataclasses import dataclass
from enum import Enum
 
class DefenseLayer(Enum):
    INPUT_CLASSIFIER = "input_classifier"
    OUTPUT_FILTER = "output_filter"
    RATE_LIMITER = "rate_limiter"
    BEHAVIORAL_MONITOR = "behavioral_monitor"
 
@dataclass
class TargetProfile:
    identified_defenses: list
    estimated_difficulty: str
    recommended_techniques: list
    bypass_candidates: list
 
def build_target_profile(recon_results: dict) -> TargetProfile:
    defenses = []
    techniques = []
    if recon_results.get("avg_latency_increase", 0) > 1.5:
        defenses.append(DefenseLayer.INPUT_CLASSIFIER)
        techniques.append("encoding_bypass")
    if recon_results.get("truncated_responses", 0) > 0:
        defenses.append(DefenseLayer.OUTPUT_FILTER)
        techniques.append("side_channel_extraction")
    difficulty = "high" if len(defenses) >= 3 else "medium" if defenses else "low"
    return TargetProfile(
        identified_defenses=defenses,
        estimated_difficulty=difficulty,
        recommended_techniques=techniques,
        bypass_candidates=[d for d in defenses if d != DefenseLayer.RATE_LIMITER],
    )

class FindingReport:
    def __init__(self, title: str, severity: str, description: str):
        self.title = title
        self.severity = severity
        self.description = description
        self.owasp_mapping = ""
        self.mitre_mapping = ""
        self.steps_to_reproduce = []
        self.recommendations = []
 
    def add_step(self, step: str) -> None:
        self.steps_to_reproduce.append(step)
 
    def add_recommendation(self, rec: str) -> None:
        self.recommendations.append(rec)
 
    def to_markdown(self) -> str:
        steps = "\n".join(f"{i+1}. {s}" for i, s in enumerate(self.steps_to_reproduce))
        recs = "\n".join(f"- {r}" for r in self.recommendations)
        return f"## {self.title}\n\n**Severity**: {self.severity}\n\n### Description\n{self.description}\n\n### Steps\n{steps}\n\n### Recommendations\n{recs}"