audit-augmentation

Files (4)

• SKILL.md
• openai.yaml · agents/
• trail-of-bits-mark.svg · assets/
• formats.md · references/

SKILL.md (first lines)

# 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:

```bash
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:

```python
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

```bash
# 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

```python
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

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