Graph Evolution

Graph Evolution

Builds Trailmark code graphs at two source snapshots and computes a structural diff. Surfaces security-relevant changes that text-level diffs miss: new attack paths, complexity shifts, blast radius growth, taint propagation changes, and privilege boundary modifications.

When to Use

• Comparing two git refs to understand what structurally changed
• Auditing a range of commits for security-relevant evolution
• Detecting new attack paths created by code changes
• Finding functions whose blast radius or complexity grew silently
• Identifying taint propagation changes across refactors
• Pre-release structural comparison (tag-to-tag or branch-to-branch)

When NOT to Use

• Line-level code review (use differential-review for text-diff analysis)
• Single-snapshot analysis (use the trailmark skill directly)
• Diagram generation from a single snapshot (use the diagramming-code skill)
• Mutation testing triage (use the genotoxic skill)

Rationalizations to Reject

Prerequisites

trailmark must be installed. If uv run trailmark fails, run:

uv pip install trailmark

DO NOT fall back to "manual comparison" or reading source files as a substitute for running trailmark. The tool must be installed and used programmatically. If installation fails, report the error.

Quick Start

# Compare two git refs (e.g., tags, branches, commits)
# 1. Build graphs at each snapshot
# 2. Run pre-analysis on both
# 3. Compute structural diff
# 4. Generate report

# Step-by-step: see Workflow below

Decision Tree

├─ Need to understand what each metric means?
│  └─ Read: references/evolution-metrics.md
│
├─ Need the report output format?
│  └─ Read: references/report-format.md
│
├─ Already have two graph JSON exports?
│  └─ Jump to Phase 3 (run graph_diff.py directly)
│
└─ Starting from two git refs?
   └─ Start at Phase 1

Workflow

Graph Evolution Progress:
- [ ] Phase 1: Create snapshots (git worktrees)
- [ ] Phase 2: Build graphs + pre-analysis on both snapshots
- [ ] Phase 3: Compute structural diff
- [ ] Phase 4: Interpret diff and generate report
- [ ] Phase 5: Clean up worktrees

Phase 1: Create Snapshots

Use git worktrees to get clean copies of each ref without disturbing the working tree.

# Create temp directories for worktrees
BEFORE_DIR=$(mktemp -d)
AFTER_DIR=$(mktemp -d)

# Create worktrees (run from repo root)
git worktree add "$BEFORE_DIR" {before_ref}
git worktree add "$AFTER_DIR" {after_ref}

If comparing two directories instead of git refs, skip this phase and use the directory paths directly in Phase 2.

Phase 2: Build Graphs and Run Pre-Analysis

Build Trailmark graphs for both snapshots and run pre-analysis on each. Pre-analysis computes blast radius, taint propagation, privilege boundaries, and entrypoint enumeration.

import json
from trailmark.query.api import QueryEngine

def build_and_export(target_dir, language, output_path):
    """Build graph, run pre-analysis, export JSON."""
    engine = QueryEngine.from_directory(target_dir, language=language)
    engine.preanalysis()
    json_str = engine.to_json()
    with open(output_path, "w") as f:
        f.write(json_str)
    return engine.summary()

import tempfile, os
work_dir = tempfile.mkdtemp(prefix="trailmark_evolution_")
before_json = os.path.join(work_dir, "before_graph.json")
after_json = os.path.join(work_dir, "after_graph.json")

before_summary = build_and_export(
    "{before_dir}", "{lang}", before_json
)
after_summary = build_and_export(
    "{after_dir}", "{lang}", after_json
)

Verify both graphs built successfully by checking the summary output. If either fails, check that the language parameter matches the codebase and that trailmark supports all file types present.

Phase 3: Compute Structural Diff

Run the diff script on the two exported JSON files (using the same work_dir from Phase 2):

uv run {baseDir}/scripts/graph_diff.py \
    --before "{before_json}" \
    --after "{after_json}" > "{work_dir}/evolution_diff.json"

The output JSON contains:

Phase 4: Interpret Diff and Generate Report

Read the diff JSON and generate a security-focused markdown report. See references/report-format.md for the full template.

Interpretation priorities (highest to lowest):

1. New tainted paths — nodes entering the tainted subgraph, especially if they also appear in added edges targeting sensitive functions
2. Privilege boundary changes — new or removed trust transitions
3. Attack surface growth — new entrypoints, especially untrusted_external
4. Blast radius increases — nodes entering high_blast_radius
5. Complexity spikes — CC increases > 3 on tainted or entrypoint-reachable nodes
6. Structural additions — new nodes and edges (review needed)
7. Structural removals — verify removed security functions were replaced

Cross-reference structural changes with git diff {before_ref}..{after_ref} to add source-level context to findings.

Severity classification:

For detailed metric definitions, see references/evolution-metrics.md.

Phase 5: Clean Up

Remove git worktrees after the report is written:

git worktree remove "{before_dir}"
git worktree remove "{after_dir}"

Diff Script Reference

uv run {baseDir}/scripts/graph_diff.py [OPTIONS]

Input format: Trailmark JSON exports from engine.to_json(). Output: JSON structural diff to stdout.

Quality Checklist

Before delivering the report:

• Both graphs built successfully (check summaries)
• Pre-analysis ran on both snapshots
• Structural diff computed (non-empty diff JSON)
• All subgraph changes interpreted (tainted, blast radius, etc.)
• Critical findings include evidence (node IDs, edge diffs)
• Severity levels assigned to all findings
• Source-level context added via git diff cross-reference
• Worktrees cleaned up (or temp dirs removed)
• Report written to GRAPH_EVOLUTION_*.md

Integration

trailmark skill: Phase 2 uses the trailmark API for graph building and pre-analysis. All trailmark query patterns work on either snapshot's engine.

differential-review skill: Use graph-evolution for structural analysis, differential-review for line-level code review. The two are complementary — graph-evolution finds attack paths that text diffs miss, while differential-review provides git blame context and micro-adversarial analysis.

genotoxic skill: If graph-evolution reveals new high-CC tainted nodes, feed them to genotoxic for mutation testing triage.

diagramming-code skill: Generate before/after diagrams to visualize structural changes. Use call-graph or data-flow diagrams focused on changed nodes.

Supporting Documentation

• references/evolution-metrics.md — What each structural metric means and why it matters for security
• references/report-format.md — Report template, severity classification, and example findings