seikaikyo/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