安全审计
- 作者仓库星标 5,723
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- 作者更新于 2026年6月15日 16:05
- 作者仓库 skills
- 领域
- AI 智能
- 兼容 Agent
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- 作者 / 版本 / 许可
- @trailofbits · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- macOS · Linux · Windows
- 底层运行要求
- Node.js · Python
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: audit-augmentation
description: > Projects findings from external tools (SARIF) and human auditors (weAudit) onto Trailmark code…
category: AI 智能
runtime: Node.js / Python
---
# audit-augmentation 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“When to Use / When NOT to Use / Rationalizations to Reject”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“When to Use / When NOT to Use / Rationalizations to Reject”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件。
先用一个小任务确认它会围绕“When to Use / When NOT to Use / Rationalizations to Reject”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: audit-augmentation
description: > Projects findings from external tools (SARIF) and human auditors (weAudit) onto Trailmark code…
category: AI 智能
source: trailofbits/skills
---
# audit-augmentation
## 什么时候使用
- 把「审计」相关任务沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查、可继续迭代的步骤…
- 面向提示词、Agent 工作流、模型评估或自动化推理,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「When to Use / When NOT to Use / Rationalizations to Reject」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "audit-augmentation" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> When to Use / When NOT to Use / Rationalizations to Reject
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Node.js / Python | 读取文件、写入/修改文件 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Audit Augmentation
Projects findings from external tools (SARIF) and human auditors (weAudit) onto Trailmark code graphs as annotations and subgraphs.
When to Use
- Importing Semgrep, CodeQL, or other SARIF-producing tool results into a graph
- Importing weAudit audit annotations into a graph
- Cross-referencing static analysis findings with blast radius or taint data
- Querying which functions have high-severity findings
- Visualizing audit coverage alongside code structure
When NOT to Use
- Running static analysis tools (use semgrep/codeql directly, then import)
- Building the code graph itself (use the
trailmarkskill) - Generating diagrams (use the
diagramming-codeskill after augmenting)
Rationalizations to Reject
| Rationalization | Why It's Wrong | Required Action |
|---|---|---|
| "The user only asked about SARIF, skip pre-analysis" | Without pre-analysis, you can't cross-reference findings with blast radius or taint | Always run engine.preanalysis() before augmenting |
| "Unmatched findings don't matter" | Unmatched findings may indicate parsing gaps or out-of-scope files | Report unmatched count and investigate if high |
| "One severity subgraph is enough" | Different severities need different triage workflows | Query all severity subgraphs, not just error |
| "SARIF results speak for themselves" | Findings without graph context lack blast radius and taint reachability | Cross-reference with pre-analysis subgraphs |
| "weAudit and SARIF overlap, pick one" | Human auditors and tools find different things | Import both when available |
| "Tool isn't installed, I'll do it manually" | Manual analysis misses what tooling catches | Install trailmark first |
Installation
MANDATORY: If uv run trailmark fails, install trailmark first:
uv pip install trailmark
Quick Start
CLI
# Augment with SARIF
uv run trailmark augment {targetDir} --sarif results.sarif
# Augment with weAudit
uv run trailmark augment {targetDir} --weaudit .vscode/alice.weaudit
# Both at once, output JSON
uv run trailmark augment {targetDir} \
--sarif results.sarif \
--weaudit .vscode/alice.weaudit \
--json
Programmatic API
from trailmark.query.api import QueryEngine
engine = QueryEngine.from_directory("{targetDir}", language="auto")
# Run pre-analysis first for cross-referencing
engine.preanalysis()
# Augment with SARIF
result = engine.augment_sarif("results.sarif")
# result: {matched_findings: 12, unmatched_findings: 3, subgraphs_created: [...]}
# Augment with weAudit
result = engine.augment_weaudit(".vscode/alice.weaudit")
# Query findings
engine.findings() # All findings
engine.subgraph("sarif:error") # High-severity SARIF
engine.subgraph("weaudit:high") # High-severity weAudit
engine.subgraph("sarif:semgrep") # By tool name
engine.annotations_of("function_name") # Per-node lookup
If auto-detection is wrong for the target, rerun with an explicit language or
comma-separated list such as python,rust.
Workflow
Augmentation Progress:
- [ ] Step 1: Build graph and run pre-analysis
- [ ] Step 2: Locate SARIF/weAudit files
- [ ] Step 3: Run augmentation
- [ ] Step 4: Inspect results and subgraphs
- [ ] Step 5: Cross-reference with pre-analysis
Step 1: Build the graph and run pre-analysis for blast radius and taint context:
engine = QueryEngine.from_directory("{targetDir}", language="auto")
engine.preanalysis()
If auto-detection is wrong for the target, rerun with an explicit language or
comma-separated list such as python,rust.
Step 2: Locate input files:
- SARIF: Usually output by tools like
semgrep --sarif -o results.sariforcodeql database analyze --format=sarif-latest - weAudit: Stored in
.vscode/<username>.weauditwithin the workspace
Step 3: Run augmentation via engine.augment_sarif() or
engine.augment_weaudit(). Check unmatched_findings in the result — these
are findings whose file/line locations didn't overlap any parsed code unit.
Step 4: Query findings and subgraphs. Use engine.findings() to list all
annotated nodes. Use engine.subgraph_names() to see available subgraphs.
Step 5: Cross-reference with pre-analysis data to prioritize:
- Findings on tainted nodes: overlap
sarif:errorwithtaintedsubgraph - Findings on high blast radius nodes: overlap with
high_blast_radius - Findings on privilege boundaries: overlap with
privilege_boundary
Annotation Format
Findings are stored as standard Trailmark annotations:
- Kind:
finding(tool-generated) oraudit_note(human notes) - Source:
sarif:<tool_name>orweaudit:<author> - Description: Compact single-line:
[SEVERITY] rule-id: message (tool)
Subgraphs Created
| Subgraph | Contents |
|---|---|
sarif:error |
Nodes with SARIF error-level findings |
sarif:warning |
Nodes with SARIF warning-level findings |
sarif:note |
Nodes with SARIF note-level findings |
sarif:<tool> |
Nodes flagged by a specific tool |
weaudit:high |
Nodes with high-severity weAudit findings |
weaudit:medium |
Nodes with medium-severity weAudit findings |
weaudit:low |
Nodes with low-severity weAudit findings |
weaudit:findings |
All weAudit findings (entryType=0) |
weaudit:notes |
All weAudit notes (entryType=1) |
How Matching Works
Findings are matched to graph nodes by file path and line range overlap:
- Finding file path is normalized relative to the graph's
root_path - Nodes whose
location.file_pathmatches AND whose line range overlaps are selected - The tightest match (smallest span) is preferred
- If a finding's location doesn't overlap any node, it counts as unmatched
SARIF paths may be relative, absolute, or file:// URIs — all are handled.
weAudit uses 0-indexed lines which are converted to 1-indexed automatically.
Supporting Documentation
- references/formats.md — SARIF 2.1.0 and weAudit file format field reference
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核