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