Audit Augmentation

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

Installation

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

uv tool install trailmark
# Python snippets: uv run --with trailmark python -   (a tool env is not importable)

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

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