Files
MeshSate/app/engines/keywords.py
zhaolei 9834930299 fix: PDF/Office/压缩包关键词误报 — 两层修复
【regex 层收紧】policy.yaml keyword_rules

1) ReverseShellNetcat: \\bnc\\b → \\bnc\\s+-[elpvs]\\w*
   nc 后必须跟参数 (-e/-l/-p/-v/-s), 单独 nc 在二进制里必然误报
   ncat 同理: \\bncat\\b → \\bncat\\s+-\\w+
   保留 \\bnetcat\\b (8 字母, 碰撞概率极低)

2) CredentialStealer: 全部关键词加 \\b 词边界
   wce 单独加后续约束: wce(\\.exe\\b|\\s|\\/)
   要求 wce 后跟 .exe / 空格 / / (二进制随机字节里 WCE 不再触发)

【引擎层架构修复】keywords.py

新增 BINARY_SKIP_EXTS (48 个扩展名):
  - PDF / Office (.pdf/.docx/.xlsx/.pptx/...)
  - 压缩包 (.rar/.zip/.7z/.tar/.gz/...)
  - 图片 (.png/.jpg/.gif/.svg/...)
  - 视频 (.mp4/.avi/.mkv/...)
  - 音频 (.mp3/.wav/.flac/...)

KeywordEngine.scan() 开头检查:
  如果 declared_ext / path ext 在 BINARY_SKIP_EXTS → 直接返回空

根因:
  PDF/DOCX/ZIP 等文件是 DEFLATE 压缩流 (伪随机字节)
  短关键词 (nc=2w/wce=3w) 在压缩流里必然出现
  误报导致: 云启智能体平台生成效果.pdf 被拦截
    ReverseShellNetcat: 'g%ew;V6Z?)Nc{>72z}*&96'
    CredentialStealer: '2>>~:pa}w*WCE|2MVB85t/$'

验证:
  ✅ 新 regex: 真实 nc -e / wce.exe 命中, PDF 压缩流字节不命中
  ✅ BINARY_SKIP_EXTS: .pdf/.docx/.zip 跳过, .sh/.ps1/.txt 扫描
  ✅ scan() 方法已含跳过逻辑
2026-10-29 17:28:23 +08:00

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"""内网特征词表引擎(P1)。
数据驱动:词表从 policy.yaml 的 ``keyword_rules`` 段加载,不在代码中硬编码。
支持三类匹配模式:
* ``contains`` — 子串匹配(区分大小写)
* ``regex`` — 正则表达式匹配
* ``any_of`` — 多关键词中任一命中
用途:识别内网特有模板、密级标识、命名规律等——辅助宏例外判断和内容审计方向。
"""
from __future__ import annotations
import logging
import os
import re
from typing import Any, Dict, List
from app.engines.base import Engine, EngineResult, Finding, ScanTarget, Severity
logger = logging.getLogger(__name__)
class KeywordEngine(Engine):
"""内网特征词表匹配引擎。"""
# ---- 跳过列表:二进制 / 压缩封装 / 媒体文件 ----
# 这些文件有专门引擎处理(PDF/Office/Archive/PE 等),
# 它们的内容是压缩流或二进制格式,关键词 regex 在里面会大面积误报。
# 短关键词 (nc/wce/curl/...) 在压缩流里等于伪随机字节必然碰撞。
BINARY_SKIP_EXTS = frozenset({
# PDF / Office
".pdf", ".docx", ".docm", ".doc", ".xlsx", ".xlsm", ".xls",
".pptx", ".pptm", ".ppt", ".ppsx", ".pps", ".potx",
# 压缩包
".rar", ".zip", ".7z", ".tar", ".gz", ".bz2", ".xz", ".tgz",
".iso", ".img", ".dmg",
# 图片
".png", ".jpg", ".jpeg", ".gif", ".bmp", ".tiff", ".tif", ".webp",
".ico", ".svg",
# 视频
".mp4", ".avi", ".mkv", ".mov", ".flv", ".wmv", ".webm",
# 音频
".mp3", ".wav", ".flac", ".ogg", ".aac", ".wma", ".m4a",
})
name = "keywords"
layer = "L2B"
version = "1.0"
def __init__(self, keyword_rules: List[Dict[str, Any]] | None = None) -> None:
"""初始化。keyword_rules 来自 PolicyStore.current().keyword_rules。"""
self._rules = keyword_rules or []
# 预编译正则
self._compiled: List[Dict[str, Any]] = []
for rule in self._rules:
pattern = rule.get("pattern", "")
if rule.get("match") == "regex" and pattern:
try:
compiled = re.compile(pattern, re.IGNORECASE if rule.get("ignore_case") else 0)
except re.error:
logger.warning("词表规则正则编译失败: %s", rule.get("name"))
continue
self._compiled.append({**rule, "_re": compiled})
else:
self._compiled.append(rule)
# ------------------------------------------------------------------- 接口实现
def is_enabled(self) -> bool:
return bool(self._rules)
def scan(self, target: ScanTarget, ctx: Any) -> EngineResult:
result = EngineResult(engine=self.name)
if not self._rules:
return result
findings: List[Finding] = []
# ---- 跳过二进制/压缩封装文件 ----
# 这些文件内容是压缩流或纯二进制,短关键词 regex 在里面必然误报
ext = (target.declared_ext or "").lower()
if not ext and target.path:
ext = os.path.splitext(target.path)[1].lower()
if ext in self.BINARY_SKIP_EXTS:
return result
try:
text = self._extract_text(target.path, target.declared_ext)
except Exception: # noqa: BLE001
return result
if not text:
return result
# ---- 预处理:合并换行符以支持跨行关键词 ----
# 攻击者会把 Invoke-WebRequest 这种关键字用 CRLF 拆成
# "Invoke-Web\r\nRequest" 来绕过基于单行的匹配。
# 这里把所有 \r\n / \r / \n 替换为空,让跨行关键词重新拼接。
# 注意:不影响纯文本/多行语义,我们匹配的都是单行关键词或跨行命令。
text = text.replace("\r\n", "").replace("\r", "").replace("\n", "")
for rule in self._compiled:
match_mode = rule.get("match", "contains")
rule_name = rule.get("name", "keyword")
weight = rule.get("weight", 30.0)
severity_str = rule.get("severity", "medium")
severity = getattr(Severity, severity_str.upper(), Severity.MEDIUM).value
hits: List[str] = []
if match_mode == "regex":
compiled = rule.get("_re")
if compiled:
for m in compiled.finditer(text):
snippet = text[max(0, m.start() - 10): m.end() + 10]
hits.append(snippet.strip())
if len(hits) >= 3:
break
elif match_mode == "any_of":
keywords = rule.get("keywords", [])
for kw in keywords:
if kw.lower() in text.lower():
hits.append(kw)
if len(hits) >= 3:
break
elif match_mode == "contains":
pattern = rule.get("pattern", "")
if pattern and (pattern.lower() in text.lower()):
hits.append(pattern)
if hits:
findings.append(
Finding(
engine=self.name,
layer=self.layer,
severity=severity,
rule=rule_name,
detail=f"词表命中:{rule_name}(模式={match_mode}),命中 {len(hits)} 处:{hits[:2]}",
weight=float(weight),
)
)
result.findings = findings
result.meta["scanned_chars"] = len(text)
return result
# ------------------------------------------------------------------- 内部辅助
@staticmethod
def _extract_text(path: str, ext: str) -> str:
"""从文件中提取可匹配的文本(简化版,够用就行)。
OOXML/PDF/DOCX 等结构化文档优先交给对应的引擎处理,
词表引擎只扫纯文本、代码文件和可直接读的二进制残留字符串。
"""
text_exts = {".txt", ".csv", ".json", ".xml", ".html", ".htm",
".md", ".log", ".ini", ".cfg", ".yaml", ".yml",
".sh", ".bash", ".zsh", ".py", ".pl", ".rb", ".js", ".ts",
".bat", ".cmd", ".ps1", ".vbs", ".sql", ".go", ".java",
".c", ".cpp", ".h", ".cs"}
ext_lower = (ext or "").lower()
if ext_lower in text_exts:
try:
with open(path, "r", encoding="utf-8", errors="replace") as fh:
return fh.read(256 * 1024)
except OSError:
return ""
# 二进制文件:提取可打印字符串(≥4 字符)
try:
with open(path, "rb") as fh:
data = fh.read(512 * 1024)
except OSError:
return ""
strings: List[str] = []
current = bytearray()
for byte in data:
if 32 <= byte <= 126 or byte in (9,): # 可打印或 tab
current.append(byte)
else:
if len(current) >= 4:
strings.append(current.decode("ascii", errors="replace"))
current = bytearray()
if len(current) >= 4:
strings.append(current.decode("ascii", errors="replace"))
return "\n".join(strings)