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scripts/search_router.py:93- Finding
Simulated Search Results Are Presented as Traceable Multi-Source Research
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List[SearchResult]: """ 执行并行检索（模拟实现） Returns: 检索结果列表，按优先级排序 """ strategy = self.determine_search_strategy() results = [] # 根据策略执行检索（此处为模拟实现，实际应调用真实检索工具） for source_type, config in strategy.items(): priority = config["priority"] keywords = config["keywords"] for keyword in keywords: # 模拟检索结果 result = SearchResult( source_type=source_type, source_name=f"{source_type}_source", content=f"检索结果：{keyword} 的相关内容", priority=priority, raw_numbers=[] # 实际检索时应提取原始数值 ) results.append(result) ``` ### Technical Analysis The Skill documentation describes a workflow that performs parallel knowledge-base, API, and web searches to produce high-confidence, traceable research. The implementation does not contact any external source or retrieve evidence. Instead, it creates generic placeholder strings and assigns them authoritative-looking source types and names. The generated `SearchResult` objects are structurally indistinguishable from genuine retrieved results to downstream components. No field identifies the records as synthetic, no URL or source excerpt is required, and the method does not fail when retrieval is unavailable. This is an integrity and functional-security issue rather than malicious code. Downstream agents may treat fabricated placeholders as evidence because the output uses labels such as `knowledge_source`, `api_source`, and `web_source`. ### Attack Path 1. A user requests an industry, medical, regulatory, or technical definition. 2. The Skill selects a search strategy based on the supplied term. 3. `execute_search()` create ...[truncated 1060 chars]- Remediation
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