audit-augmentation

name

audit-augmentation

description

Augments Trailmark code graphs with external audit findings from SARIF static analysis results, weAudit annotation files, and version-gated Trailmark 0.4.x binary-analysis graph exports. Maps findings to graph nodes by file and line overlap, creates severity-based subgraphs, and enables cross-referencing findings with pre-analysis data (blast radius, taint, etc.). Use when projecting SARIF results onto a code graph, overlaying weAudit annotations, importing binary graph findings, cross-referencing Semgrep, CodeQL, or binary-analysis findings with call graph data, or visualizing audit findings in the context of code structure.

Audit Augmentation

Projects findings from external tools (SARIF) and human auditors (weAudit) onto Trailmark code graphs as annotations and subgraphs. Trailmark 0.4.0+ can also import an external binary-analysis graph JSON export via engine.augment_binary().

When to Use

• Importing Semgrep, CodeQL, or other SARIF-producing tool results into a graph
• Importing weAudit audit annotations into a graph
• Importing binary-analysis graph data into a source graph (Trailmark 0.4.0+)
• Cross-referencing static analysis findings with blast radius or taint data
• Querying which functions have high-severity findings
• Visualizing audit coverage alongside code structure
• Preparing one SARIF or weAudit result for trailmark-finding-triage

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

---

Installation

MANDATORY: If uv run trailmark fails, install trailmark first:

uv pip install trailmark

Version Gate

SARIF and weAudit augmentation are v0.2-safe. Binary graph augmentation is Trailmark 0.4.0+ only. Before calling engine.augment_binary(), check:

if not hasattr(engine, "augment_binary"):
    raise RuntimeError("Binary augmentation requires Trailmark >= 0.4.0")

On Trailmark 0.5.0+, known links between source functions and imported binary or external endpoints can also be declared once in .trailmark/links.toml (see the main trailmark skill's Repository Links section) instead of being re-derived per session. Declared external endpoints materialize as proxy.external:<symbol> nodes on every parse.

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

Binary graph augmentation is programmatic in Trailmark 0.4.0+; do not invent a CLI flag if trailmark augment --help does not show one.

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")

# Augment with an external binary graph export (v0.4+)
if hasattr(engine, "augment_binary"):
    result = engine.augment_binary("binary_graph.json")

# 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/binary graph 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
• Binary graph (v0.4+): External JSON with artifact, functions, and calls fields. Trailmark imports this graph; it does not disassemble binaries itself.

Step 3: Run augmentation via engine.augment_sarif() or engine.augment_weaudit(). For binary graphs, run engine.augment_binary() only after the Version Gate succeeds. Check unmatched_findings in SARIF and weAudit results — 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

For one candidate finding that needs a reachability verdict or PoC handoff, continue with trailmark-finding-triage and use the augmented node as the bound candidate.

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)
binary:<artifact>	Binary function nodes imported from a v0.4+ binary graph

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.

Binary graph imports create origin=binary function nodes, origin=proxy external proxy nodes for unresolved binary calls, and inferred corresponds_to edges when a binary function maps back to a source node. The expected JSON shape is intentionally small:

{
  "artifact": {"name": "libexample", "architecture": "x86_64", "sha256": "..."},
  "functions": [
    {"symbol": "parse_packet", "address": "0x401000",
     "source": {"file": "src/parser.c", "line": 42}}
  ],
  "calls": [
    {"source": "parse_packet", "target": "malloc", "confidence": "inferred"}
  

Supporting Documentation

• references/formats.md — SARIF 2.1.0 and weAudit file format field reference