audit-augmentation

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 trailmark skill)
• Generating diagrams (use the diagramming-code skill 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

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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.sarif or codeql database analyze --format=sarif-latest
• weAudit: Stored in .vscode/<username>.weaudit within 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:error with tainted subgraph
• 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) or audit_note (human notes)
• Source: sarif:<tool_name> or weaudit:<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:

1. Finding file path is normalized relative to the graph's root_path
2. Nodes whose location.file_path matches AND whose line range overlaps are selected
3. The tightest match (smallest span) is preferred
4. 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