trailmark

---
name: trailmark
description: "Builds and queries multi-language source code graphs for security analysis. Includes pre-analysis passes for blast radius, taint propagation, privilege boundaries, and entry point enumeration. Use when analyzing call paths, mapping attack surface, finding complexity hotspots, enumerating entry points, tracing taint propagation, measuring blast radius, or building a code graph for audit prioritization. Prefer `trailmark.parse.detect_languages()` or `--language auto` when the target language is unknown or polyglot."
---

# Trailmark

Parses source code into a directed graph of functions, classes, calls, and
semantic metadata for security analysis.

## When to Use

- Mapping call paths from user input to sensitive functions
- Finding complexity hotspots for audit prioritization
- Identifying attack surface and entrypoints
- Understanding call relationships in unfamiliar codebases
- Security review or audit preparation across polyglot projects
- Adding LLM-inferred annotations (assumptions, preconditions) to code units
- Pre-analysis before mutation testing (genotoxic skill) or diagramming

## When NOT to Use

- Single-file scripts where call graph adds no value (read the file directly)
- Architecture diagrams not derived from code (use the `diagramming-code` skill or draw by hand)
- Mutation testing triage (use the genotoxic skill, which calls trailmark internally)
- Runtime behavior analysis (trailmark is static, not dynamic)

## Rationalizations to Reject

| Rationalization | Why It's Wrong | Required Action |
|-----------------|----------------|-----------------|
| "I'll just read the source files manually" | Manual reading misses call paths, blast radius, and taint data | Install trailmark and use the API |
| "Pre-analysis isn't needed for a quick query" | Blast radius, taint, and privilege data are only available after `preanalysis()` | Always run `engine.preanalysis()` before handing off to other skills |
| "The graph is too large, I'll sample" | Sampling misses cross-module attack paths | Build the full graph; use subgraph queries to focus |
| "Uncertain edges don't matter" | Dynamic dispatch is where type confusion bugs hide | Account for `uncertain` edges in security claims |
| "Single-language analysis is enough" | Polyglot repos have FFI boundaries where bugs cluster | Use the correct `--language` flag per component |
| "Complexity hotspots are the only thing worth checking" | Low-complexity functions on tainted paths are high-value targets | Combine complexity with taint and blast radius data |

---

## Installation

**MANDATORY:** If `uv run trailmark` fails (command not found, import error,
ModuleNotFoundError), install trailmark before doing anything else:

```bash
uv pip install trailmark
```

**DO NOT** fall back to "manual verification", "manual analysis", or reading
source files by hand as a substitute for running trailmark. The tool must be
installed and used programmatically. If installation fails, report the error
to the user instead of silently switching to manual code reading.

## Quick Start

```bash
# Auto-detect and merge every supported language under the tree
uv run trailmark analyze --language auto --summary {targetDir}

# Explicit languages (single language or comma-separated list)
uv run trailmark analyze --language rust {targetDir}
uv run trailmark analyze --language python,rust {targetDir}

# Complexity hotspots
uv run trailmark analyze --language auto --complexity 10 {targetDir}
```

### Programmatic API

```python
from trailmark.parse import detect_languages, supported_languages
from trailmark.query.api import QueryEngine

# Ask the installed Trailmark build what it supports
supported_languages()
detect_languages("{targetDir}")

# Prefer auto for unknown or polyglot trees; use explicit lists when needed
engine = QueryEngine.from_directory("{targetDir}", language="auto")
engine = QueryEngine.from_directory("{targetDir}", language="python,rust")

engine.callers_of("function_name")
engine.callees_of("function_name")
engine.paths_between("entry_func", "db_query")
engine.complexity_hotspots(threshold=10)
engine.attack_surface()
engine.summary()
engine.to_json()

# Run pre-analysis (blast radius, entrypoints, privilege
# boundaries, taint propagation)
result = engine.preanalysis()

# Query subgraphs created by pre-analysis
engine.subgraph_names()
engine.subgraph("tainted")
engine.subgraph("high_blast_radius")
engine.subgraph("privilege_boundary")
engine.subgraph("entrypoint_reachable")

# Add LLM-inferred annotations
from trailmark.models import AnnotationKind

engine.annotate("function_name", AnnotationKind.ASSUMPTION,
                "input is URL-encoded", source="llm")

# Query annotations (including pre-analysis results)
engine.annotations_of("function_name")
engine.annotations_of("function_name",
                       kind=AnnotationKind.BLAST_RADIUS)
engine.annotations_of("function_name",
                       kind=AnnotationKind.TAINT_PROPAGATION)
```

## Pre-Analysis Passes

**Always run `engine.preanalysis()` before handing off to genotoxic or
`diagramming-code` skills.** Pre-analysis enriches the graph with four passes:

1. **Blast radius estimation** — counts downstream and upstream nodes per
   function, identifies critical high-complexity descendants
2. **Entry point enumeration** — maps entrypoints by trust level, computes
   reachable node sets
3. **Privilege boundary detection** — finds call edges where trust levels
   change (untrusted -> trusted)
4. **Taint propagation** — marks all nodes reachable from untrusted
   entrypoints

Results are stored as annotations and named subgraphs on the graph.

For detailed documentation, see
[references/preanalysis-passes.md](references/preanalysis-passes.md).

## Language Selection

Do not hardcode a stale language table in downstream workflows. Ask the
installed Trailmark build what it supports:

```python
from trailmark.parse import detect_languages, supported_languages

supported_languages()
detect_languages("{targetDir}")
```

CLI patterns:

```bash
# Auto-detect and merge
uv run trailmark analyze --language auto {targetDir}

# Explicit list for a known polyglot target
uv run trailmark analyze --language python,rust {targetDir}
```

## Graph Model

**Node kinds:** `function`, `method`, `class`, `module`, `struct`,
`interface`, `trait`, `enum`, `namespace`, `contract`, `library`,
`template`

**Edge kinds:** `calls`, `inherits`, `implements`, `contains`, `imports`

**Edge confidence:** `certain` (direct call, `self.method()`), `inferred`
(attribute access on non-self object), `uncertain` (dynamic dispatch)

### Per Code Unit
- Parameters with types, return types, exception types
- Cyclomatic complexity and branch metadata
- Docstrings
- Annotations: `assumption`, `precondition`, `postcondition`, `invariant`,
  `blast_radius`, `privilege_boundary`, `taint_propagation`, `finding`,
  `audit_note` (last two set by `augment_sarif` / `augment_weaudit`)

### Per Edge
- Source/target node IDs, edge kind, confidence level

### Project Level
- Dependencies (imported packages)
- Entrypoints with trust levels and asset values
- Named subgraphs (populated by pre-analysis)

## Key Concepts

**Declared contract vs. effective input domain:** Trailmark separates what a
function *declares* it accepts from what can *actually reach* it via call
paths. Mismatches are where vulnerabilities hide:
- **Widening**: Unconstrained data reaches a function that assumes validation
- **Safe by coincidence**: No validation, but only safe callers exist today

**Edge confidence:** Dynamic dispatch produces `uncertain` edges. Account for
confidence when making security claims.

**Subgraphs:** Named collections of node IDs produced by pre-analysis.
Query with `engine.subgraph("name")`. Available after `engine.preanalysis()`.

## Query Patterns

See [references/query-patterns.md](references/query-patterns.md) for common
security analysis patterns.

See [references/preanalysis-passes.md](references/preanalysis-passes.md) for
pre-analysis pass documentation.