Audit Context Building

TL;DR

• 目的：Understand a codebase before looking for bugs in it - what each function assumes, what it guarantees, and what it depends on elsewhere. Use…
• 适用：代码层审计
• 输入：目标代码库路径 + 关注的模块/接口
• 输出：配置审计报告 + 修复建议
• 红线：仅限授权范围内；扫描限速 -c 10 -rl 10；所有动作记 oplog
• 关联：上游：003-src-session-start → 下游：158-c-review, 160-rust-review, 161-static-analysis

Audit Context Building

Build understanding, not verdicts. This runs before anyone hunts for bugs, and feeds that work.

Quick Start

# 阅读代码库结构
ls -la src/
find . -type f -name "*.py" | head -20
# 识别入口点
grep -rn "def main\|__main__" src/

When to Use

At the start of an audit, a threat model, or an architecture review, when the code is unfamiliar. Also when an earlier pass produced findings nobody could judge, because no one had mapped out how the system fits together.

When NOT to Use

Do not name vulnerabilities, suggest fixes, write proofs-of-concept, or rate severity. Those belong to the hunting phase, which runs next and with the whole picture in hand. When the code counts on something and nothing checks it, record that plainly and move on — whether it matters is decided later.

Not worth the tokens on code you already understand.

Do not analyze in this context

The analysis is long, and this context needs to survive to use it. Dispatch it:

• A codebase, or more than one function — run /audit-context-building:audit-context <path>. It orients, analyzes each function in its own subagent, and writes audit-context/DOSSIER.md plus one file per function under audit-context/functions/. Only compact records return here.
• A single function — dispatch the audit-context-building:function-analyzer agent at it. It writes its prose to disk and returns a record.

Then work from what comes back: the index, the unenforced assumptions, the open questions. Read a function's file when you need its detail.

The workflow is what enforces this, not this text: a subagent bound to a return schema cannot return prose. Treat this section as routing, and route.

What comes back, and how to read it

Each record lists what must always be true (with the line that shows it), what the function takes on faith (with whatever establishes it), which functions it calls and what it needs from each, and anything still unclear. The dossier adds the rules that span several functions, who can reach what, and where the complicated parts cluster.

Two things matter more than the rest:

• Assumptions marked nothing found. The code counts on something being true and nothing anywhere makes it true. This is the most useful thing to hand the hunting phase.
• The open questions. An honest list of what is still unclear beats a confident answer that turns out to be wrong. Carry them forward instead of closing them out.

Where two records disagree, both are quoted rather than quietly reconciled. That is a fact about the code, not a flaw in the analysis.

The format

ANALYSIS_FORMAT.md defines it, and FUNCTION_MICRO_ANALYSIS_EXAMPLE.md works through examples in C and Solidity. Read them when extending this plugin or deciding whether a record can be trusted.

The format is the same whatever the target. What changes is what fills each slot, and what counts as a call you cannot see inside. DOMAIN_NOTES.md maps that across smart contracts, C and C++, decompiled firmware, and web services — read it when the target is not plain source code.

The rule that matters most: follow the calls. Whether a function is correct usually depends on something another function does, and you cannot see that from the caller alone. A limit looks enforced because the value came back from a function whose name suggests it was checked. So read the function being called, follow every path through it rather than only the one that succeeds, and say what makes each assumption true. When nothing does, use those words: nothing found. Every claim cites a line, or becomes an open question.

Advanced Techniques

Semgrep Custom Rules

Author organization-specific Semgrep rules for internal framework misuse patterns.

Taint Analysis

Use CodeQL taint tracking to follow user input from source to sink across function boundaries.