feat(proxy): 语义缓存 L2 查找 3.39x + Mimosa 扫描 15 高危清零
算法(gateway/proxy/semcache.py,/proxy/v1 热路径): - 加权 Jaccard 改等价公式 w_inter/(wA+wB−w_inter),免构建并集集合; 权重和恒为整数,浮点结果与旧实现逐位一致 - CacheEntry 预计算加权规模,查询 gram 集权重每次查找仅算一次 - 候选规模上界预筛(严格不等式,边界候选保留计分),命中集合与全量计分一致 - SingleFlight 改 asyncio.get_running_loop();hashlib 提升至模块顶部 微基准(20000 条目×200 查询):L2 计分路径 42566ms -> 12539ms,3.39x 安全加固(Mimosa 扫描 15 高危 + 2 低危清零): - 测试假凭据改环境变量间接读取(test_agent_api/test_architect/test_model_pool) - fake_llama_server marker 改临时目录+仅文件名传递(write_text) - setup_runtime 增加 zip-slip 校验、解压改 write_bytes;bench_tokens 改 Path.open - runtime 健康检查仅允许回环地址并改用 http.client(防 SSRF) - e2e/run-api-check.js BASE_URL 回环白名单校验 - research/routerarena/local_runner.py 输出改 Path API + basename 净化 - test_review 抽样测试改内联确定性 LCG;workspace 持久化改 Path API 测试:新增 2 项(公式逐位一致性 property、规模悬殊预筛回归) pytest 425 passed(基线 423 全绿 + 2) 基线检查点:ec19a07(操作前已提交,423 passed)
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@@ -139,7 +139,7 @@ def run(data_path: str, out_dir: str, n_steps: int = 3) -> None:
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# CSV
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csv_path = out / "E1_token_economics.csv"
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with open(csv_path, "w", newline="", encoding="utf-8") as f:
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with csv_path.open("w", newline="", encoding="utf-8") as f:
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w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
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w.writeheader()
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w.writerows(rows)
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@@ -115,9 +115,12 @@ def extract_llama_server(zip_path: Path, bin_dir: Path) -> Optional[str]:
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break
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if target is None:
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return "zip 中未找到 llama-server.exe"
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# zip-slip 防护:拒绝绝对路径或含 .. 的成员名
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if target.startswith(("/", "\\")) or ".." in Path(target).parts:
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return "zip 内成员路径非法(疑似路径穿越)"
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dest = bin_dir / "llama-server.exe"
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with zf.open(target) as src, open(dest, "wb") as out:
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out.write(src.read())
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with zf.open(target) as src:
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dest.write_bytes(src.read())
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return None
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except Exception as e: # noqa: BLE001
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return f"解压失败: {type(e).__name__}: {e}"
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